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    <description>recent bookmarks from cshalizi</description>
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  </channel><item rdf:about="https://www.cambridge.org/core/journals/philosophy-of-science/article/noncausal-explanations-of-social-and-biological-networks/59E80990AA755C7CBBD6E543832681C4?WT.mc_id=New%2520Cambridge%2520Alert%2520-%2520Articles">
    <title>Non-causal Explanations of Social and Biological Networks | Philosophy of Science | Cambridge Core</title>
    <dc:date>2026-06-24T12:54:36+00:00</dc:date>
    <link>https://www.cambridge.org/core/journals/philosophy-of-science/article/noncausal-explanations-of-social-and-biological-networks/59E80990AA755C7CBBD6E543832681C4?WT.mc_id=New%2520Cambridge%2520Alert%2520-%2520Articles</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["One argument that all explanations are causal explanations is that no extant analysis of non-causal explanations can respect mandatory restrictions on explanation. After articulating two such restrictions, Asymmetry and Directionality, we consider how they work with respect to an explanation from network theory that applies to both social and brain networks. We argue that there are two viable ways to make sense of this explanation. One approach is broadly ontic, while another is pragmatic."]]></description>
<dc:subject>to:NB philosophy_of_science networks explanation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ab6037e35687/</dc:identifier>
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<item rdf:about="https://www.sciencedirect.com/science/article/pii/S0378873325000668?via%3Dihub">
    <title>Use of aggregated relational data in agent-based modeling - ScienceDirect</title>
    <dc:date>2026-02-17T16:00:59+00:00</dc:date>
    <link>https://www.sciencedirect.com/science/article/pii/S0378873325000668?via%3Dihub</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Aggregated relational data (ARD) provides valuable information for inferring structural features of personal social networks at scale. Following recent ARD studies, we suggest a formal parameter for agent-based modeling (ABM) that helps reflect multiple structural features of extended social networks (e.g., size; variation; distribution) and apply it to a widely known classic ABM—Axelrod’s cultural dynamic model. Results show that when incorporating realistic network features estimated from ARD, the model generates outcomes substantially different from its original results. Our study highlights ARD's potential to enrich ABM in reflecting more realistic networks that better connect micro-processes with macro-phenomena."]]></description>
<dc:subject>to:NB agent-based_models networks axelrod_model network_data_analysis</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f3b72b309c49/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:agent-based_models"/>
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<item rdf:about="https://www.tandfonline.com/doi/full/10.1080/0022250X.2024.2423941">
    <title>Full article: Cultural and opinion dynamics in small-world “social” networks</title>
    <dc:date>2026-02-17T16:00:16+00:00</dc:date>
    <link>https://www.tandfonline.com/doi/full/10.1080/0022250X.2024.2423941</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This study shows the importance of considering two social network features in agent-based models of cultural and opinion dynamics – network segregation by groups (e.g. race, class, or ideology) and interaction frequency by structural embeddedness. I formalize the two features as modeling conditions and apply them to an existing model that shows cultural or opinion polarization can emerge in a small-world network by the bridging of long-range ties. I find that when a small-world network is segregated, the inactivity of long-range ties (i.e. infrequent interactions) – instead of the bridging – becomes the key feature that contributes conversely to consensus or more extreme polarization. This implies that a systemic understanding of dyadic-level tie characteristics and suitable approaches to considering social networks in agent-based models are necessary."]]></description>
<dc:subject>to:NB agent-based_models polarization networks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:aa28a304db4e/</dc:identifier>
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<item rdf:about="https://arxiv.org/abs/2212.01987">
    <title>[2212.01987] Fractal dimensions for Iterated Graph Systems</title>
    <dc:date>2025-12-03T20:39:06+00:00</dc:date>
    <link>https://arxiv.org/abs/2212.01987</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Building upon [1], this study aims to introduce fractal geometry into graph theory, and to establish a potential theoretical foundation for complex networks. Specifically, we employ the method of substitution to create and explore fractal-like graphs, termed deterministic or random iterated graph systems. While the concept of substitution is commonplace in fractal geometry and dynamical systems, its analysis in the context of graph theory remains a nascent field.
"By delving into the properties of these systems, including diameter and distal, we derive two primary outcomes. Firstly, within the deterministic iterated graph systems, we establish that the Minkowski dimension and Hausdorff dimension align analytically through explicit formulae. Secondly, in the case of random iterated graph systems, we demonstrate that almost every graph limit exhibits identical Minkowski and Hausdorff dimensions numerically by their Lyapunov exponents.
"The exploration of iterated graph systems holds the potential to unveil novel directions. These findings not only, mathematically, contribute to our understanding of the interplay between fractals and graphs, but also, physically, suggest promising avenues for applications for complex networks."]]></description>
<dc:subject>to:NB fractals networks graph_theory re:fractal_network_asymptotics to_read scooped? via:vaguery</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:0d244921ef96/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:fractals"/>
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<item rdf:about="https://direct.mit.edu/books/oa-monograph/5866/The-Connectivity-of-ThingsNetwork-Cultures-since">
    <title>The Connectivity of Things: Network Cultures since 1832 | Books Gateway | MIT Press</title>
    <dc:date>2025-09-24T15:31:46+00:00</dc:date>
    <link>https://direct.mit.edu/books/oa-monograph/5866/The-Connectivity-of-ThingsNetwork-Cultures-since</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["A media history of the material and infrastructural features of networking practices, a German classic translated for the first time into English.
"Nets hold, connect, and catch. They ensnare, bind, and entangle. Our social networks owe their name to a conceivably strange and ambivalent object. But how did the net get into the network? And how can it reasonably represent the connectedness of people, things, institutions, signs, infrastructures, and even nature? The Connectivity of Things by Sebastian Giessmann, the first media history that addresses the overwhelming diversity of networks, attempts to answer all these questions and more.
"Reconstructing the decisive moments in which networking turned into a veritable cultural technique, Giessmann takes readers below the street to the Parisian sewers and to the Suez Canal, into the telephone exchanges of Northeast America, and on to the London Underground. His brilliant history explains why social networks were discovered late, how the rapid rise of mathematical network theory was able to take place, how improbable the invention of the internet was, and even what diagrams and conspiracy theories have to do with it all. A primer on networking as a cultural technique, this translated German classic explains everything one ever could wish to know about networks."

--- I am most interested in this for the promised history of network diagrams.]]></description>
<dc:subject>to:NB books:noted networks history_of_technology visual_display_of_quantitative_information downloaded</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:7041adc9e602/</dc:identifier>
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<item rdf:about="https://www.annualreviews.org/content/journals/10.1146/annurev-biodatasci-080917-013444">
    <title>Network Analysis as a Grand Unifier in Biomedical Data Science | Annual Reviews</title>
    <dc:date>2025-04-09T14:54:58+00:00</dc:date>
    <link>https://www.annualreviews.org/content/journals/10.1146/annurev-biodatasci-080917-013444</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Biomedical data scientists study many types of networks, ranging from those formed by neurons to those created by molecular interactions. People often criticize these networks as uninterpretable diagrams termed hairballs; however, here we show that molecular biological networks can be interpreted in several straightforward ways. First, we can break down a network into smaller components, focusing on individual pathways and modules. Second, we can compute global statistics describing the network as a whole. Third, we can compare networks. These comparisons can be within the same context (e.g., between two gene regulatory networks) or cross-disciplinary (e.g., between regulatory networks and governmental hierarchies). The latter comparisons can transfer a formalism, such as that for Markov chains, from one context to another or relate our intuitions in a familiar setting (e.g., social networks) to the relatively unfamiliar molecular context. Finally, key aspects of molecular networks are dynamics and evolution, i.e., how they evolve over time and how genetic variants affect them. By studying the relationships between variants in networks, we can begin to interpret many common diseases, such as cancer and heart disease."]]></description>
<dc:subject>to:NB network_data_analysis networks biochemical_networks via:aks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:30e36c1f0a02/</dc:identifier>
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<item rdf:about="https://arxiv.org/abs/2503.04020">
    <title>[2503.04020] An Approximate-Master-Equation Formulation of the Watts Threshold Model on Hypergraphs</title>
    <dc:date>2025-04-09T14:12:55+00:00</dc:date>
    <link>https://arxiv.org/abs/2503.04020</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In traditional models of behavioral or opinion dynamics on social networks, researchers suppose that all interactions occur between pairs of individuals. However, in reality, social interactions also occur in groups of three or more individuals. A common way to incorporate such polyadic interactions is to study dynamical processes on hypergraphs. In a hypergraph, interactions can occur between any number of the individuals in a network. The Watts threshold model (WTM) is a well-known model of a simplistic social spreading process. Very recently, Chen et al. extended the WTM from dyadic networks (i.e., graphs) to polyadic networks (i.e., hypergraphs). In the present paper, we extend their discrete-time model to continuous time using approximate master equations (AMEs). By using AMEs, we are able to model the system with very high accuracy. We then reduce the high-dimensional AME system to a system of three coupled differential equations without any detectable loss of accuracy. This much lower-dimensional system is more computationally efficient to solve numerically and is also easier to interpret. We linearize the reduced AME system and calculate a cascade condition, which allows us to determine when a large spreading event occurs. We then apply our model to a social contact network of a French primary school and to a hypergraph of computer-science coauthorships. We find that the AME system is accurate in modelling the polyadic WTM on these empirical networks; however, we expect that future work that incorporates structural correlations between nearby nodes and groups into the model for the dynamics will lead to more accurate theory for real-world networks."]]></description>
<dc:subject>to:NB social_influence stochastic_processes networks porter.mason_a. re:do-institutions-evolve</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:8a3aab250ab3/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:porter.mason_a."/>
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<item rdf:about="https://www.aeaweb.org/articles?id=10.1257/aer.20201425">
    <title>Product Differentiation and Oligopoly: A Network Approach - American Economic Association</title>
    <dc:date>2025-03-31T23:43:45+00:00</dc:date>
    <link>https://www.aeaweb.org/articles?id=10.1257/aer.20201425</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["I present a new theory of oligopoly and markups in general equilibrium, based on an innovative, scalable hedonic demand system, which I take to the data for the universe of US public firms. In my model, firms compete in a network of product market rivalries that emerge endogenously out of the characteristics of the products they supply. I estimate that consumer surplus is almost three times as large as profits; decompose firm-level markups into metrics of quality-adjusted productivity and market centrality; and analyze the extent, evolution, and drivers of monopoly power in the United States between 1995 and 2021."]]></description>
<dc:subject>economic_networks in_NB economics networks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:beb574477f44/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
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</item>
<item rdf:about="https://arxiv.org/abs/2503.18823">
    <title>[2503.18823] Coarse-graining Directed Networks with Ergodic Sets Preserving Diffusive Dynamics</title>
    <dc:date>2025-03-31T23:42:53+00:00</dc:date>
    <link>https://arxiv.org/abs/2503.18823</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In this paper, we introduce ergodic sets, subsets of nodes of the networks that are dynamically disjoint from the rest of the network (i.e. that can never be reached or left following to the network dynamics). We connect their definition to purely structural considerations of the network and study some of their basic properties. We study numerically the presence of such structures in a number of synthetic network models and in classes of networks from a variety of real-world applications, and we use them to present a compression algorithm that preserve the random walk diffusive dynamics of the original network."

--- "Can never be left" would not be so interesting, in fact it'd be pretty classical.  "Can never be reached" will either make this interesting or trivial.]]></description>
<dc:subject>to:NB networks graph_theory</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b70e1137cb6c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graph_theory"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://link.springer.com/article/10.1007/s13194-023-00549-2">
    <title>How do networks explain? A neo-hempelian approach to network explanations of the ecology of the microbiome | European Journal for Philosophy of Science</title>
    <dc:date>2025-03-10T13:34:35+00:00</dc:date>
    <link>https://link.springer.com/article/10.1007/s13194-023-00549-2</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Despite the importance of network analysis in biological practice, dominant models of scientific explanation do not account satisfactorily for how this family of explanations gain their explanatory power in every specific application. This insufficiency is particularly salient in the study of the ecology of the microbiome. Drawing on Coyte et al. (2015) study of the ecology of the microbiome, Deulofeu et al. (2021) argue that these explanations are neither mechanistic, nor purely mathematical, yet they are substantially empirical. Building on their criticisms, in the present work we make a step further elucidating this kind of explanations with a general analytical framework according to which scientific explanations are ampliative, specialized embeddings (ASE), which has recently been successfully applied to other biological subfields. We use ASE to reconstruct in detail the Coyte et al.’s case study and on its basis, we claim that network explanations of the ecology of the microbiome, and other similar explanations in ecology, gain their epistemological force in virtue of their capacity to embed biological phenomena in non-accidental generalizations that are simultaneously ampliative and specialized."]]></description>
<dc:subject>to:NB philosophy_of_science networks explanation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:45fbb1d684df/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:explanation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2206.07514">
    <title>[2206.07514] Networks of reinforced stochastic processes: a complete description of the first-order asymptotics</title>
    <dc:date>2023-06-28T16:17:48+00:00</dc:date>
    <link>https://arxiv.org/abs/2206.07514</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We consider a finite collection of reinforced stochastic processes with a general network-based interaction among them. We provide sufficient and necessary conditions in order to have some form of almost sure asymptotic synchronization, which could be roughly defined as the almost sure long-run uniformization of the behavior of interacting processes. Specifically, we detect a regime of complete synchronization, where all the processes converge toward the same random variable, a second regime where the system almost surely converges, but there exists no form of almost sure asymptotic synchronization, and another regime where the system does not converge with a strictly positive probability. In this latter case, partitioning the system in cyclic classes according to the period of the interaction matrix, we have an almost sure asymptotic synchronization within the cyclic classes, and, with a strictly positive probability, an asymptotic periodic behavior of these classes."]]></description>
<dc:subject>stochastic_processes networks in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:64ecdf9574da/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2303.09198">
    <title>[2303.09198] Large deviations for triangles in scale-free random graphs</title>
    <dc:date>2023-04-27T14:47:23+00:00</dc:date>
    <link>https://arxiv.org/abs/2303.09198</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We provide large deviations estimates for the upper tail of the number of triangles in scale-free inhomogeneous random graphs where the degrees have power law tails with index −α,α∈(1,2). We show that upper tail probabilities for triangles undergo a phase transition. For α<4/3, the upper tail is caused by many vertices of degree of order n, and this probability is semi-exponential. In this regime, additional triangles consist of two hubs. For α>4/3 on the other hand, the upper tail is caused by one hub of a specific degree, and this probability decays polynomially in n, leading to additional triangles with one hub. In the intermediate case α=4/3, we show polynomial decay of the tail probability caused by multiple but finitely many hubs. In this case, the additional triangles contain either a single hub or two hubs. Our proofs are partly based on various concentration inequalities. In particular, we tailor concentration bounds for empirical processes to make them well-suited for analyzing heavy-tailed phenomena in nonlinear settings."]]></description>
<dc:subject>networks large_deviations heavy_tails in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:0742d3d44f46/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:large_deviations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:heavy_tails"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2304.05794">
    <title>[2304.05794] Systemic risk measured by systems resiliency to initial shocks</title>
    <dc:date>2023-04-27T14:45:08+00:00</dc:date>
    <link>https://arxiv.org/abs/2304.05794</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The study of systemic risk is often presented through the analysis of several measures referring to quantities used by practitioners and policy makers. Almost invariably, those measures evaluate the size of the impact that exogenous events can exhibit on a financial system without analysing the nature of initial shock. Here we present a symmetric approach and propose a set of measures that are based on the amount of exogenous shock that can be absorbed by the system before it starts to deteriorate. For this purpose, we use a linearized version of DebtRank that allows to clearly show the onset of financial distress towards a correct systemic risk estimation. We show how we can explicitly compute localized and uniform exogenous shocks and explained their behavior though spectral graph theory. We also extend analysis to heterogeneous shocks that have to be computed by means of Monte Carlo simulations. We believe that our approach is more general and natural and allows to express in a standard way the failure risk in financial systems."]]></description>
<dc:subject>to:NB networks dynamical_systems</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:cfa6a95f2b6b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dynamical_systems"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://link.springer.com/article/10.1007/BF01025993">
    <title>Intrinsic fluctuations and a phase transition in a class of large populations of interacting oscillators | SpringerLink</title>
    <dc:date>2023-04-24T22:02:59+00:00</dc:date>
    <link>https://link.springer.com/article/10.1007/BF01025993</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["A theory of intrinsic fluctuations is developed of a phase ordering parameter for large populations of weakly and uniformly coupled limit-cycle oscillators with distributed native frequencies. In particular it is shown that the intensity as well as the correlation time of fluctuations exhibit power-law divergence at the onset of mutual entrainment with critical exponents which depend on whether the coupling strength approaches the threshold from below or above. This peculiar feature is demonstrated by numerical simulations mainly through finite-size scaling analyses. In the course of exploring its origin, we encounter a new concept termed a “correlation frequency” which provides a natural interpretation of the finite-size scaling laws. A comment is given on a recent theory by Kuramoto and Nishikawa to clarify why it contradicts our results."]]></description>
<dc:subject>to:NB networks dynamical_systems synchronization phase_transitions cleaning_out_the_filing_cabinet_for_the_first_time_since_2005 have_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:7d2f206d0789/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dynamical_systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:synchronization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:phase_transitions"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cleaning_out_the_filing_cabinet_for_the_first_time_since_2005"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.pnas.org/doi/10.1073/pnas.2215752120">
    <title>Strong connectivity in real directed networks | PNAS</title>
    <dc:date>2023-03-21T15:38:23+00:00</dc:date>
    <link>https://www.pnas.org/doi/10.1073/pnas.2215752120</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In many real, directed networks, the strongly connected component of nodes which are mutually reachable is very small. This does not fit with current theory, based on random graphs, according to which strong connectivity depends on mean degree and degree–degree correlations. And it has important implications for other properties of real networks and the dynamical behavior of many complex systems. We find that strong connectivity depends crucially on the extent to which the network has an overall direction or hierarchical ordering—a property measured by trophic coherence. Using percolation theory, we find the critical point separating weakly and strongly connected regimes and confirm our results on many real-world networks, including ecological, neural, trade, and social networks. We show that the connectivity structure can be disrupted with minimal effort by a targeted attack on edges which run counter to the overall direction. This means that many dynamical processes on networks can depend significantly on a small fraction of edges."]]></description>
<dc:subject>to:NB percolation_theory graph_theory networks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:4e85d56f5328/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:percolation_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graph_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.nature.com/articles/s41467-023-37019-5">
    <title>The dynamic nature of percolation on networks with triadic interactions | Nature Communications</title>
    <dc:date>2023-03-17T18:13:08+00:00</dc:date>
    <link>https://www.nature.com/articles/s41467-023-37019-5</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Percolation establishes the connectivity of complex networks and is one of the most fundamental critical phenomena for the study of complex systems. On simple networks, percolation displays a second-order phase transition; on multiplex networks, the percolation transition can become discontinuous. However, little is known about percolation in networks with higher-order interactions. Here, we show that percolation can be turned into a fully fledged dynamical process when higher-order interactions are taken into account. By introducing signed triadic interactions, in which a node can regulate the interactions between two other nodes, we define triadic percolation. We uncover that in this paradigmatic model the connectivity of the network changes in time and that the order parameter undergoes a period doubling and a route to chaos. We provide a general theory for triadic percolation which accurately predicts the full phase diagram on random graphs as confirmed by extensive numerical simulations. We find that triadic percolation on real network topologies reveals a similar phenomenology. These results radically change our understanding of percolation and may be used to study complex systems in which the functional connectivity is changing in time dynamically and in a non-trivial way, such as in neural and climate networks.]]></description>
<dc:subject>to:NB percolation networks hypergraphs re:do-institutions-evolve</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b5aef56bffd8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:percolation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:hypergraphs"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2212.12839">
    <title>[2212.12839] Escape times for subgraph detection and graph partitioning</title>
    <dc:date>2023-01-18T03:12:24+00:00</dc:date>
    <link>https://arxiv.org/abs/2212.12839</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We provide a rearrangement based algorithm for fast detection of subgraphs of k vertices with long escape times for directed or undirected networks. Complementing other notions of densest subgraphs and graph cuts, our method is based on the mean hitting time required for a random walker to leave a designated set and hit the complement. We provide a new relaxation of this notion of hitting time on a given subgraph and use that relaxation to construct a fast subgraph detection algorithm and a generalization to K-partitioning schemes. Using a modification of the subgraph detector on each component, we propose a graph partitioner that identifies regions where random walks live for comparably large times. Importantly, our method implicitly respects the directed nature of the data for directed graphs while also being applicable to undirected graphs. We apply the partitioning method for community detection to a large class of model and real-world data sets."]]></description>
<dc:subject>to:NB networks network_data_analysis community_discovery stochastic_processes mucha.peter</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f14fb18e0235/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:network_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:community_discovery"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mucha.peter"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://academic.oup.com/restud/advance-article-abstract/doi/10.1093/restud/rdac077/6835695?redirectedFrom=fulltext&amp;login=false">
    <title>LEARNING FROM NEIGHBORS ABOUT A CHANGING STATE | The Review of Economic Studies | Oxford Academic</title>
    <dc:date>2022-12-27T19:10:27+00:00</dc:date>
    <link>https://academic.oup.com/restud/advance-article-abstract/doi/10.1093/restud/rdac077/6835695?redirectedFrom=fulltext&amp;login=false</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Agents learn about a changing state using private signals and their neighbors’ past estimates of the state. We present a model in which Bayesian agents in equilibrium use neighbors’ estimates simply by taking weighted sums with time-invariant weights. The dynamics thus parallel those of the tractable DeGroot model of learning in networks, but arise as an equilibrium outcome rather than a behavioral assumption. We examine whether information aggregation is nearly optimal as neighborhoods grow large. A key condition for this is signal diversity: each individual’s neighbors have private signals that not only contain independent information, but also have sufficiently different distributions. Without signal diversity—e.g., if private signals are i.i.d.—learning is suboptimal in all networks and highly inefficient in some. Turning to social influence, we find it is much more sensitive to one’s signal quality than to one’s number of neighbors, in contrast to standard models with exogenous updating rules."]]></description>
<dc:subject>to:NB social_learning networks golub.ben social_life_of_the_mind</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:6403057a4722/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:golub.ben"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_life_of_the_mind"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.science.org/doi/10.1126/sciadv.abm8310">
    <title>Network structural origin of instabilities in large complex systems | Science Advances</title>
    <dc:date>2022-08-31T23:22:25+00:00</dc:date>
    <link>https://www.science.org/doi/10.1126/sciadv.abm8310</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["A central issue in the study of large complex network systems, such as power grids, financial networks, and ecological systems, is to understand their response to dynamical perturbations. Recent studies recognize that many real networks show nonnormality and that nonnormality can give rise to reactivity—the capacity of a linearly stable system to amplify its response to perturbations, oftentimes exciting nonlinear instabilities. Here, we identify network structural properties underlying the pervasiveness of nonnormality and reactivity in real directed networks, which we establish using the most extensive dataset of such networks studied in this context to date. The identified properties are imbalances between incoming and outgoing network links and paths at each node. On the basis of this characterization, we develop a theory that quantitatively predicts nonnormality and reactivity and explains the observed pervasiveness. We suggest that these results can be used to design, upgrade, control, and manage networks to avoid or promote network instabilities."]]></description>
<dc:subject>to:NB dynamical_systems networks motter.adilson</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:4311381b447f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dynamical_systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:motter.adilson"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://journals.aps.org/rmp/abstract/10.1103/RevModPhys.94.015005">
    <title>Rev. Mod. Phys. 94, 015005 (2022) - Collective nonlinear dynamics and self-organization in decentralized power grids</title>
    <dc:date>2022-06-06T13:04:31+00:00</dc:date>
    <link>https://journals.aps.org/rmp/abstract/10.1103/RevModPhys.94.015005</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The ongoing transition to renewable energy supply comes with a restructuring of power grids, changing their effective interaction topologies, more and more strongly decentralizing them and substantially modifying their input, output, and response characteristics. All of these changes imply that power grids become increasingly affected by collective, nonlinear dynamic phenomena, structurally and dynamically more distributed and less predictable in space and time, more heterogeneous in its building blocks, and as a consequence less centrally controllable. Here cornerstone aspects of data-driven and mathematical modeling of collective dynamical phenomena emerging in real and model power grid networks by combining theories from nonlinear dynamics, stochastic processes and statistical physics, anomalous statistics, optimization, and graph theory are reviewed. The mathematical background required for adequate modeling and analysis approaches is introduced, an overview of power system models is given, and a range of collective dynamical phenomena are focused on, including synchronization and phase locking, flow (re)routing, Braess’s paradox, geometric frustration, and spreading and localization of perturbations and cascading failures, as well as the nonequilibrium dynamics of power grids, where fluctuations play a pivotal role."]]></description>
<dc:subject>dynamical_systems networks self-organization synchronization kurths.jurgen re:blackouts_and_alienation in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:436a84efd0d7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dynamical_systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:self-organization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:synchronization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kurths.jurgen"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:blackouts_and_alienation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2203.06601">
    <title>[2203.06601] Dynamics on higher-order networks: A review</title>
    <dc:date>2022-06-06T12:57:44+00:00</dc:date>
    <link>https://arxiv.org/abs/2203.06601</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Network science has evolved into an indispensable platform for studying complex systems. But recent research has identified limits of classical networks, where links connect pairs of nodes, to comprehensively describe group interactions. Higher-order networks, where a link can connect more than two nodes, have therefore emerged as a new frontier in network science. Since group interactions are common in social, biological, and technological systems, higher-order networks have recently led to important new discoveries across many fields of research. We here review these works, focusing in particular on the novel aspects of the dynamics that emerges on higher-order networks. We cover a variety of dynamical processes that have thus far been studied, including different synchronization phenomena, contagion processes, the evolution of cooperation, and consensus formation. We also outline open challenges and promising directions for future research."]]></description>
<dc:subject>to:NB networks dynamical_systems</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:39465def7bde/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dynamical_systems"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.nature.com/articles/d41586-022-01254-5">
    <title>Weak links in finance and supply chains are easily weaponized</title>
    <dc:date>2022-05-09T20:24:44+00:00</dc:date>
    <link>https://www.nature.com/articles/d41586-022-01254-5</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[--- As I told Henry, I am profoundly glad there will never be a global crisis calling for expertise in information-theoretic limits to prediction.  (We are not actually living in a Stanislaw Lem novel.)]]></description>
<dc:subject>weaponized_interdependence political_economy networks kith_and_kin farrell.henry have_read to_teach:baby-nets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:14d74b2d7b54/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:weaponized_interdependence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:political_economy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:farrell.henry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:baby-nets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2106.13220">
    <title>[2106.13220] Forget Partitions: Cluster Synchronization in Directed Networks Generate Hierarchies</title>
    <dc:date>2021-06-28T04:32:18+00:00</dc:date>
    <link>https://arxiv.org/abs/2106.13220</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We present a scalable approach for simplifying the stability analysis of cluster synchronization patterns on directed networks. When a network has directional couplings, decomposition of the coupling matrix into independent blocks (which in turn decouples the variational equation) is no longer adequate to reveal the full relations among perturbation modes. Instead, it is often necessary to introduce directional dependencies among the blocks and establish hierarchies among perturbation modes. For this purpose, we develop an algorithm that finds the simultaneous block upper triangularization of sets of asymmetric matrices, which generalizes the Jordan canonical decomposition from a single matrix to an arbitrary number of matrices. The block upper triangularization orders subspaces of the variational equation in a directional manner, allowing the stability of perturbation modes to be analyzed in sequence. We show that our algorithm gives the greatest possible simplification under mild assumptions, both in terms of the sizes of the blocks and in terms of the number of nonzero upper triangular entries linking the blocks."]]></description>
<dc:subject>to:NB synchronization networks community_discovery</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:6cdd173c1c21/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:synchronization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:community_discovery"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2106.03523">
    <title>[2106.03523] A stylised view on structural and functional connectivity in dynamical processes in networks</title>
    <dc:date>2021-06-10T02:03:41+00:00</dc:date>
    <link>https://arxiv.org/abs/2106.03523</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The relationship of network structure and dynamics is one of most extensively investigated problems in the theory of complex systems of the last years. Understanding this relationship is of relevance to a range of disciplines -- from Neuroscience to Geomorphology. A major strategy of investigating this relationship is the quantitative comparison of a representation of network architecture (structural connectivity) with a (network) representation of the dynamics (functional connectivity). Analysing such SC/FC relationships has over the past years contributed substantially to our understanding of the functional role of network properties, such as modularity, hierarchical organization, hubs and cycles.
"Here, we show that one can distinguish two classes of functional connectivity -- one based on simultaneous activity (co-activity) of nodes the other based on sequential activity of nodes. We delineate these two classes in different categories of dynamical processes -- excitations, regular and chaotic oscillators -- and provide examples for SC/FC correlations of both classes in each of these models. We expand the theoretical view of the SC/FC relationships, with conceptual instances of the SC and the two classes of FC for various application scenarios in Geomorphology, Freshwater Ecology, Systems Biology, Neuroscience and Social-Ecological Systems.
"Seeing the organization of a dynamical processes in a network either as governed by co-activity or by sequential activity allows us to bring some order in the myriad of observations relating structure and function of complex networks."]]></description>
<dc:subject>to:NB dynamical_systems networks functional_connectivity synchronization</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:23617071a49b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dynamical_systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:functional_connectivity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:synchronization"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2105.08892">
    <title>[2105.08892] A Phase Transition in Large Network Games</title>
    <dc:date>2021-05-20T14:06:23+00:00</dc:date>
    <link>https://arxiv.org/abs/2105.08892</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In this paper, we use a model of large random network game where the agents plays selfishly and are affected by their neighbors, to explore the conditions under which the Nash equilibrium (NE) of the game is affected by a perturbation in the network. We use a phase transition phenomenon observed in finite rank deformations of large random matrices, to study how the NE changes on crossing critical threshold points. Our main contribution is as follows: when the perturbation strength is greater than a critical point, it impacts the NE of the game, whereas when this perturbation is below this critical point, the NE remains independent of the perturbation parameter. This demonstrates a phase transition in NE which alludes that perturbations can affect the behavior of the society only if their strength is above a critical threshold. We provide numerical examples for this result and present scenarios under which this phenomenon could potentially occur in real world applications."

--- If equilibria really are unchanged by sufficiently small perturbations, one should be able to partition network space into equivalence classes of networks with the same equilibria, and forget about the details of the network structure, which would be very convenient.  But it seems too good to be true.]]></description>
<dc:subject>to:NB game_theory networks phase_transitions re:do-institutions-evolve color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:767bb83ef51e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:game_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:phase_transitions"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2105.04437">
    <title>[2105.04437] Global hierarchy vs. local structure: spurious self-feedback in scale-free networks</title>
    <dc:date>2021-05-13T14:26:11+00:00</dc:date>
    <link>https://arxiv.org/abs/2105.04437</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Networks with fat-tailed degree distributions are omnipresent across many scientific disciplines. Such systems are characterized by so-called hubs, specific nodes with high numbers of connections to other nodes. By this property, they are expected to be key to the collective network behavior, e.g., in Ising models on such complex topologies. This applies in particular to the transition into a globally ordered network state, which thereby proceeds in a hierarchical fashion, and with a non-trivial local structure. Standard mean-field theory of Ising models on scale-free networks underrates the presence of the hubs, while nevertheless providing remarkably reliable estimates for the onset of global order. Here, we expose that a spurious self-feedback effect, inherent to mean-field theory, underlies this apparent paradox. More specifically, we demonstrate that higher order interaction effects precisely cancel the self-feedback on the hubs, and we expose the importance of hubs for the distinct onset of local versus global order in the network. Due to the generic nature of our arguments, we expect the mechanism that we uncover for the archetypal case of Ising networks of the Barabási-Albert type to be also relevant for other systems with a strongly hierarchical underlying network structure."]]></description>
<dc:subject>to:NB ising_model networks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:4b3678c16dd2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ising_model"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2007.04890">
    <title>[2007.04890] Emergent stability in complex network dynamics</title>
    <dc:date>2021-05-13T14:11:22+00:00</dc:date>
    <link>https://arxiv.org/abs/2007.04890</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The stable functionality of networked systems is a hallmark of their natural ability to coordinate between their multiple interacting components. Yet, strikingly, real-world networks seem random and highly irregular, apparently lacking any design for stability. What then are the naturally emerging organizing principles of complex-system stability? Encoded within the system's stability matrix, the Jacobian, the answer is obscured by the scale and diversity of the relevant systems, their broad parameter space, and their nonlinear interaction mechanisms. To make advances, here we uncover emergent patterns in the structure of the Jacobian, rooted in the interplay between the network topology and the system's intrinsic nonlinear dynamics. These patterns help us analytically identify the few relevant control parameters that determine a system's dynamic stability. Complex systems, we find, exhibit discrete stability classes, from asymptotically unstable, where stability is unattainable, to sensitive, in which stability abides within a bounded range of the system's parameters. Most crucially, alongside these two classes, we uncover a third class, asymptotically stable, in which a sufficiently large and heterogeneous network acquires a guaranteed stability, independent of parameters, and therefore insensitive to external perturbation. Hence, two of the most ubiquitous characteristics of real-world networks - scale and heterogeneity - emerge as natural organizing principles to ensure stability in the face of changing environmental conditions."]]></description>
<dc:subject>to:NB dynamical_systems networks color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:444ba86026f9/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dynamical_systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.annualreviews.org/doi/abs/10.1146/annurev-control-072020-084434">
    <title>Analysis and Interventions in Large Network Games | Annual Review of Control, Robotics, and Autonomous Systems</title>
    <dc:date>2021-05-06T13:49:30+00:00</dc:date>
    <link>https://www.annualreviews.org/doi/abs/10.1146/annurev-control-072020-084434</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We review classic results and recent progress on equilibrium analysis, dynamics, and optimal interventions in network games with both continuous and discrete strategy sets. We study strategic interactions in deterministic networks as well as networks generated from a stochastic network formation model. For the former case, we review a unifying framework for analysis based on the theory of variational inequalities. For the latter case, we highlight how knowledge of the stochastic network formation model can be used by a central planner to design interventions for large networks in a computationally efficient manner when exact network data are not available."]]></description>
<dc:subject>to:NB dynamical_systems networks control_theory_and_control_engineering game_theory re:in_soviet_union_optimization_problem_solves_you</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:4256d40d9911/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dynamical_systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:control_theory_and_control_engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:game_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:in_soviet_union_optimization_problem_solves_you"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.annualreviews.org/doi/abs/10.1146/annurev-control-061820-083817">
    <title>Model Reduction Methods for Complex Network Systems | Annual Review of Control, Robotics, and Autonomous Systems</title>
    <dc:date>2021-05-06T13:48:38+00:00</dc:date>
    <link>https://www.annualreviews.org/doi/abs/10.1146/annurev-control-061820-083817</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Network systems consist of subsystems and their interconnections and provide a powerful framework for the analysis, modeling, and control of complex systems. However, subsystems may have high-dimensional dynamics and a large number of complex interconnections, and it is therefore relevant to study reduction methods for network systems. Here, we provide an overview of reduction methods for both the topological (interconnection) structure of a network and the dynamics of the nodes while preserving structural properties of the network. We first review topological complexity reduction methods based on graph clustering and aggregation, producing a reduced-order network model. Next, we consider reduction of the nodal dynamics using extensions of classical methods while preserving the stability and synchronization properties. Finally, we present a structure-preserving generalized balancing method for simultaneously simplifying the topological structure and the order of the nodal dynamics."]]></description>
<dc:subject>to:NB dynamical_systems networks dimension_reduction graph_theory</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b03640a0c4ff/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dynamical_systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dimension_reduction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graph_theory"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2104.10736">
    <title>[2104.10736] Network diffusion capacity unveiled by dynamical paths</title>
    <dc:date>2021-04-23T03:08:13+00:00</dc:date>
    <link>https://arxiv.org/abs/2104.10736</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Improving the understanding of diffusive processes in networks with complex topologies is one of the main challenges of today's complexity science. Each network possesses an intrinsic diffusive potential that depends on its structural connectivity. However, the diffusion of a process depends not only on this topological potential but also on the dynamical process itself. Quantifying this potential will allow the design of more efficient systems in which it is necessary either to weaken or to enhance diffusion. Here we introduce a measure, the {\em diffusion capacity}, that quantifies, through the concept of dynamical paths, the potential of an element of the system, and also, of the system itself, to propagate information. Among other examples, we study a heat diffusion model and SIR model to demonstrate the value of the proposed measure. We found, in the last case, that diffusion capacity can be used as a predictor of the evolution of the spreading process. In general, we show that the diffusion capacity provides an efficient tool to evaluate the performance of systems, and also, to identify and quantify structural modifications that could improve diffusion mechanisms."]]></description>
<dc:subject>to:NB networks epidemics_on_networks color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:7a14d9cbe2f4/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:epidemics_on_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2006.10421">
    <title>[2006.10421] Complex networks with tuneable dimensions as a universality playground</title>
    <dc:date>2021-04-22T15:25:37+00:00</dc:date>
    <link>https://arxiv.org/abs/2006.10421</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Universality is one of the key concepts in understanding critical phenomena. However, for interacting inhomogeneous systems described by complex networks a clear understanding of the relevant parameters for universality is still missing. Here we discuss the role of a fundamental network parameter for universality, the spectral dimension. For this purpose, we construct a complex network model where the probability of a bond between two nodes is proportional to a power law of the nodes' distances. By explicit computation we prove that the spectral dimension for this model can be tuned continuously from 1 to infinity, and we discuss related network connectivity measures. We propose our model as a tool to probe universal behaviour on inhomogeneous structures and comment on the possibility that the universal behaviour of correlated models on such networks mimics the one of continuous field theories in fractional Euclidean dimensions."]]></description>
<dc:subject>to:NB networks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:81aa383f0652/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2104.09299">
    <title>[2104.09299] Complex networks of interacting stochastic tipping elements: cooperativity of phase separation in the large-system limit</title>
    <dc:date>2021-04-21T19:45:48+00:00</dc:date>
    <link>https://arxiv.org/abs/2104.09299</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Tipping elements in the Earth System receive increased scientific attention over the recent years due to their nonlinear behavior and the risks of abrupt state changes. While being stable over a large range of parameters, a tipping element undergoes a drastic shift in its state upon an additional small parameter change when close to its tipping point. Recently, the focus of research broadened towards emergent behavior in networks of tipping elements, like global tipping cascades triggered by local perturbations. Here, we analyze the response to the perturbation of a single node in a system that initially resides in an unstable equilibrium. The evolution is described in terms of coupled nonlinear equations for the cumulants of the distribution of the elements. We show that drift terms acting on individual elements and offsets in the coupling strength are sub-dominant in the limit of large networks, and we derive an analytical prediction for the evolution of the expectation (i.e., the first cumulant). It behaves like a single aggregated tipping element characterized by a dimensionless parameter that accounts for the network size, its overall connectivity, and the average coupling strength. The resulting predictions are in excellent agreement with numerical data for Erdös-Rényi, Barabási-Albert and Watts-Strogatz networks of different size and with different coupling parameters."]]></description>
<dc:subject>to:NB dynamical_systems networks macro_from_micro re:do-institutions-evolve to_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f7b3f6fc4328/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dynamical_systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2104.07260">
    <title>[2104.07260] Quantifying firm-level economic systemic risk from nation-wide supply networks</title>
    <dc:date>2021-04-16T20:11:18+00:00</dc:date>
    <link>https://arxiv.org/abs/2104.07260</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Crises like COVID-19 or the Japanese earthquake in 2011 exposed the fragility of corporate supply networks. The production of goods and services is a highly interdependent process and can be severely impacted by the default of critical suppliers or customers. While knowing the impact of individual companies on national economies is a prerequisite for efficient risk management, the quantitative assessment of the involved economic systemic risks (ESR) is hitherto practically non-existent, mainly because of a lack of fine-grained data in combination with coherent methods. Based on a unique value added tax dataset we derive the detailed production network of an entire country and present a novel approach for computing the ESR of all individual firms. We demonstrate that a tiny fraction (0.035%) of companies has extraordinarily high systemic risk impacting about 23% of the national economic production should any of them default. Firm size alone cannot explain the ESR of individual companies; their position in the production networks does matter substantially. If companies are ranked according to their economic systemic risk index (ESRI), firms with a rank above a characteristic value have very similar ESRI values, while for the rest the rank distribution of ESRI decays slowly as a power-law; 99.8% of all companies have an impact on less than 1% of the economy. We show that the assessment of ESR is impossible with aggregate data as used in traditional Input-Output Economics. We discuss how simple policies of introducing supply chain redundancies can reduce ESR of some extremely risky companies."
]]></description>
<dc:subject>to:NB economics networks color_me_skeptical heavy_tails</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:92322f90a941/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:heavy_tails"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2104.05711">
    <title>[2104.05711] The world-wide waste web</title>
    <dc:date>2021-04-14T14:44:41+00:00</dc:date>
    <link>https://arxiv.org/abs/2104.05711</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Globally, 7-10 billion tonnes of waste are produced annually, including 300-500 million tonnes of hazardous wastes (HW)--explosive, flammable, toxic, corrosive, and infective ones. About 10 % of these HW are traded through a world-wide waste web (W4). The volume of HW traded through the W4 in the last 30 years has grown by 500 % and will continue to grow, creating serious legal, economic, environmental and health problems at global scale. Here we investigate the tip of the iceberg of the W4 by studying networks of 108 categories of wastes traded among 163 countries in the period 2003-2009. Although, most of the HW were traded between developed nations, a disproportionate asymmetry existed in the flow of waste from developed to developing countries. Using a dynamical model we simulate how waste congestion propagates through the W4. We identify 32 countries with poor environmental performance which are at high risk of waste congestion. Therefore, they are a threat of improper handling and disposal of HW. We found contamination by heavy metals (HM), by volatile organic compounds (VOC) and/or by persistent organic pollutants (POP), which were used as chemical fingerprints (CF) of the improper handling of HW in 94 % of these countries."

--- The dynamical simulation sounds weird, but the data set sounds cool (if perhaps depressing).]]></description>
<dc:subject>to:NB economics networks data_sets garbage environmental_management to_teach:baby-nets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:4e485f17b3f2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:garbage"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:environmental_management"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:baby-nets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2101.11761">
    <title>[2101.11761] Percolation on complex networks: Theory and application</title>
    <dc:date>2021-03-15T06:14:55+00:00</dc:date>
    <link>https://arxiv.org/abs/2101.11761</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In the last two decades, network science has blossomed and influenced various fields, such as statistical physics, computer science, biology and sociology, from the perspective of the heterogeneous interaction patterns of components composing the complex systems. As a paradigm for random and semi-random connectivity, percolation model plays a key role in the development of network science and its applications. On the one hand, the concepts and analytical methods, such as the emergence of the giant cluster, the finite-size scaling, and the mean-field method, which are intimately related to the percolation theory, are employed to quantify and solve some core problems of networks. On the other hand, the insights into the percolation theory also facilitate the understanding of networked systems, such as robustness, epidemic spreading, vital node identification, and community detection. Meanwhile, network science also brings some new issues to the percolation theory itself, such as percolation of strong heterogeneous systems, topological transition of networks beyond pairwise interactions, and emergence of a giant cluster with mutual connections. So far, the percolation theory has already percolated into the researches of structure analysis and dynamic modeling in network science. Understanding the percolation theory should help the study of many fields in network science, including the still opening questions in the frontiers of networks, such as networks beyond pairwise interactions, temporal networks, and network of networks. The intention of this paper is to offer an overview of these applications, as well as the basic theory of percolation transition on network systems."]]></description>
<dc:subject>to:NB networks percolation_theory epidemics_on_networks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:8a6f2b2626f4/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:percolation_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:epidemics_on_networks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2101.00863">
    <title>[2101.00863] The Atlas for the Aspiring Network Scientist</title>
    <dc:date>2021-02-26T07:35:24+00:00</dc:date>
    <link>https://arxiv.org/abs/2101.00863</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Network science is the field dedicated to the investigation and analysis of complex systems via their representations as networks. We normally model such networks as graphs: sets of nodes connected by sets of edges and a number of node and edge attributes. This deceptively simple object is the starting point of never-ending complexity, due to its ability to represent almost every facet of reality: chemical interactions, protein pathways inside cells, neural connections inside the brain, scientific collaborations, financial relations, citations in art history, just to name a few examples. If we hope to make sense of complex networks, we need to master a large analytic toolbox: graph and probability theory, linear algebra, statistical physics, machine learning, combinatorics, and more.
"This book aims at providing the first access to all these tools. It is intended as an "Atlas", because its interest is not in making you a specialist in using any of these techniques. Rather, after reading this book, you will have a general understanding about the existence and the mechanics of all these approaches. You can use such an understanding as the starting point of your own career in the field of network science. This has been, so far, an interdisciplinary endeavor. The founding fathers of this field come from many different backgrounds: mathematics, sociology, computer science, physics, history, digital humanities, and more. This Atlas is charting your path to be something different from all of that: a pure network scientist."]]></description>
<dc:subject>to:NB networks network_data_analysis color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:37d1964a994c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:network_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.cambridge.org/core/journals/network-science/article/abs/singleseed-cascades-on-clustered-networks/2ED83D72F618BFCB8743D64AB7EE55AA">
    <title>Single-seed cascades on clustered networks | Network Science | Cambridge Core</title>
    <dc:date>2021-02-05T22:25:39+00:00</dc:date>
    <link>https://www.cambridge.org/core/journals/network-science/article/abs/singleseed-cascades-on-clustered-networks/2ED83D72F618BFCB8743D64AB7EE55AA</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We consider a dynamic network cascade process developed by Duncan Watts applied to a class of random networks, developed independently by Newman and Miller, which allows a specified amount of clustering (short loops). We adapt existing methods for locally tree-like networks to formulate an appropriate two-type branching process to describe the spread of a cascade started with a single active node and obtain a fixed-point equation to implicitly express the extinction probability of such a cascade. In so doing, we also recover a formula that has appeared in various forms in works by Hackett et al. and Miller which provides a threshold condition for certain extinction of the cascade. We find that clustering impedes cascade propagation for networks of low average degree by reducing the connectivity of the network, but for networks with high average degree, the presence of small cycles makes cascades more likely."]]></description>
<dc:subject>networks information_cascades contagion stochastic_processes in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:26b335892882/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_cascades"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:contagion"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.annualreviews.org/doi/abs/10.1146/annurev-ecolsys-012220-120819">
    <title>The Structure of Ecological Networks Across Levels of Organization | Annual Review of Ecology, Evolution, and Systematics</title>
    <dc:date>2021-01-03T19:20:56+00:00</dc:date>
    <link>https://www.annualreviews.org/doi/abs/10.1146/annurev-ecolsys-012220-120819</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Interactions connect the units of ecological systems, forming networks. Individual-based networks characterize variation in niches among individuals within populations. These individual-based networks merge with each other, forming species-based networks and food webs that describe the architecture of ecological communities. Networks at broader spatiotemporal scales portray the structure of ecological interactions across landscapes and over macroevolutionary time. Here, I review the patterns observed in ecological networks across multiple levels of biological organization. A fundamental challenge is to understand the amount of interdependence as we move from individual-based networks to species-based networks and beyond. Despite the uneven distribution of studies, regularities in network structure emerge across scales due to the fundamental architectural patterns shared by complex networks and the interplay between traits and numerical effects. I illustrate the integration of these organizational scales by exploring the consequences of the emergence of highly connected species for network structures across scales."]]></description>
<dc:subject>to:NB ecology networks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2b30f0f486e4/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ecology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2012.12702">
    <title>[2012.12702] Systemic Risk in Financial Networks: A Survey</title>
    <dc:date>2020-12-26T17:43:35+00:00</dc:date>
    <link>https://arxiv.org/abs/2012.12702</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We provide an overview of the relationship between financial networks and systemic risk. We present a taxonomy of different types of systemic risk, differentiating between direct externalities between financial organizations (e.g., defaults, correlated portfolios and firesales), and perceptions and feedback effects (e.g., bank runs, credit freezes). We also discuss optimal regulation and bailouts, measurements of systemic risk and financial centrality, choices by banks' regarding their portfolios and partnerships, and the changing nature of financial networks."]]></description>
<dc:subject>to:NB networks finance jackson.matthew_o.</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:bd94715620f0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:finance"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:jackson.matthew_o."/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.cambridge.org/core/elements/network-turn/CC38F2EA9F51A6D1AFCB7E005218BBE5">
    <title>The Network Turn</title>
    <dc:date>2020-12-13T23:09:40+00:00</dc:date>
    <link>https://www.cambridge.org/core/elements/network-turn/CC38F2EA9F51A6D1AFCB7E005218BBE5</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We live in a networked world. Online social networking platforms and the World Wide Web have changed how society thinks about connectivity. Because of the technological nature of such networks, their study has predominantly taken place within the domains of computer science and related scientific fields. But arts and humanities scholars are increasingly using the same kinds of visual and quantitative analysis to shed light on aspects of culture and society hitherto concealed. This Element contends that networks are a category of study that cuts across traditional academic barriers, uniting diverse disciplines through a shared understanding of complexity in our world. Moreover, we are at a moment in time when it is crucial that arts and humanities scholars join the critique of how large-scale network data and advanced network analysis are being harnessed for the purposes of power, surveillance, and commercial gain. This title is also available as Open Access on Cambridge Core."

--- Why am I learning about this from someone's pinboard feed, rather than from Scott?]]></description>
<dc:subject>to:NB to_read books:noted networks history_of_ideas weingart.scott_b. kith_and_kin via:rvenkat</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:7783ddb1abbc/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:history_of_ideas"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:weingart.scott_b."/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:rvenkat"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2012.02870">
    <title>[2012.02870] Propagation of chaos and large deviations in mean-field models with jumps on block-structured networks</title>
    <dc:date>2020-12-10T04:10:08+00:00</dc:date>
    <link>https://arxiv.org/abs/2012.02870</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["A system of interacting multiclass finite-state jump processes is analyzed. The model under consideration consists of a block-structured network with dynamically changing multi-colors nodes. The interaction is local and described through local empirical measures. Two levels of heterogeneity are considered: between and within the blocks where the nodes are labeled into two types. The central nodes are those connected only to the nodes of the same block whereas the peripheral nodes are connected to both the nodes of the same block and to some nodes from other blocks. The limits of such systems as the number of particles tends to infinity are investigated. Under regularity conditions on the peripheral nodes, propagation of chaos and law of large numbers are established in a multi-population setting. In particular, it is shown that, as the number of nodes goes to infinity, the behavior of the different classes of nodes can be represented by the solution of a McKean-Vlasov system. Moreover, we prove large deviation principles for the vectors of empirical measures and the empirical processes."]]></description>
<dc:subject>to:NB networks interacting_particle_systems large_deviations stochastic_processes</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:9e39b38e35fa/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:interacting_particle_systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:large_deviations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2002.09922">
    <title>[2002.09922] Steering complex networks toward desired dynamics</title>
    <dc:date>2020-12-02T01:45:51+00:00</dc:date>
    <link>https://arxiv.org/abs/2002.09922</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We consider networks of dynamical units that evolve in time according to different laws, and are coupled to each other in highly irregular ways. Studying how to steer the dynamics of such systems towards a desired evolution is of great practical interest in many areas of science, as well as providing insight into the interplay between network structure and dynamical behavior. We propose a pinning protocol for imposing specific dynamic evolutions compatible with the equations of motion on a networked system. The method does not impose any restrictions on the local dynamics, which may vary from node to node, nor on the interactions between nodes, which may adopt in principle any nonlinear mathematical form and be represented by weighted, directed or undirected, links. We first explore our method on small synthetic networks of chaotic oscillators, which allows us to unveil a correlation between the ordered sequence of pinned nodes and their topological influence in the network. We then consider a 12-species trophic web network, which is a model of a mammalian food web. By pinning a relatively small number of species, one can make the system abandon its spontaneous evolution from its (typically uncontrolled) initial state towards a target dynamics, or periodically control it so as to make the populations evolve within stipulated bounds. The relevance of these findings for environment management and conservation is discussed."]]></description>
<dc:subject>to:NB control_theory_and_control_engineering dynamical_systems networks re:in_soviet_union_optimization_problem_solves_you</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:e392bf3ff108/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:control_theory_and_control_engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dynamical_systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:in_soviet_union_optimization_problem_solves_you"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2011.12983">
    <title>[2011.12983] Best response dynamics on random graphs</title>
    <dc:date>2020-11-30T03:03:12+00:00</dc:date>
    <link>https://arxiv.org/abs/2011.12983</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We consider evolutionary games on a population whose underlying topology of interactions is determined by a binomial random graph G(n,p). Our focus is on 2-player symmetric games with 2 strategies played between the incident members of such a population. Players update their strategies synchronously. At each round, each player selects the strategy that is the best response to the current set of strategies its neighbours play. We show that such a system reduces to generalised majority and minority dynamics. We show rapid convergence to unanimity for p in a range that depends on a certain characteristic of the payoff matrix. In the presence of a bias among the pure Nash equilibria of the game, we determine a sharp threshold on p above which the largest connected component reaches unanimity with high probability. For p below this critical value, where this does not happen, we identify those substructures inside the largest component that remain discordant throughout the evolution of the system."]]></description>
<dc:subject>to:NB learning_in_games networks evolutionary_game_theory re:do-institutions-evolve to_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3f40d5d4ec4a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_in_games"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:evolutionary_game_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2011.11122">
    <title>[2011.11122] Controlling symmetries and clustered dynamics of complex networks</title>
    <dc:date>2020-11-25T15:41:09+00:00</dc:date>
    <link>https://arxiv.org/abs/2011.11122</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Symmetries are an essential feature of complex networks as they regulate how the graph collective dynamics organizes into clustered states. We here show how to control network symmetries, and how to enforce patterned states of synchronization with nodes clustered in a desired way. Our approach consists of perturbing the original network connectivity, either by adding new edges or by adding/removing links together with modifying their weights. By solving suitable optimization problems, we furthermore guarantee that changes made on the existing topology are minimal. The conditions for the stability of the enforced pattern are derived for the general case, and the performance of the method is illustrated with paradigmatic examples. Our results are relevant to all the practical situations in which coordination of the networked systems into diverse groups may be desirable, such as for teams of robots, unmanned autonomous vehicles, power grids and central pattern generators."]]></description>
<dc:subject>to:NB dynamical_systems networks control_theory_and_control_engineering re:in_soviet_union_optimization_problem_solves_you</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ece0572ed180/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dynamical_systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:control_theory_and_control_engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:in_soviet_union_optimization_problem_solves_you"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2006.08427">
    <title>[2006.08427] Random graphs with arbitrary clustering and their applications</title>
    <dc:date>2020-11-23T17:38:08+00:00</dc:date>
    <link>https://arxiv.org/abs/2006.08427</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The structure of many real networks is not locally tree-like and hence, network analysis fails to characterise their bond percolation properties. In a recent paper [P. Mann, V. A. Smith, J. B. O. Mitchell, and S. Dobson, Percolation in random graphs with higher-order clustering, arXiv e-prints, p. arXiv:2006.06744, June 2020.], we developed analytical solutions to the percolation properties of random networks with homogeneous clustering (clusters whose nodes are degree-equivalent). In this paper, we extend this model to investigate networks that contain clusters whose nodes are not degree-equivalent, including multilayer networks. Through numerical examples we show how this method can be used to investigate the properties of random complex networks with arbitrary clustering, extending the applicability of the configuration model and generating function formulation."]]></description>
<dc:subject>to:NB network_data_analysis networks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:88c4fa8e4234/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:network_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.tandfonline.com/doi/full/10.1080/01621459.2020.1782219">
    <title>Network Dependence Can Lead to Spurious Associations and Invalid Inference: Journal of the American Statistical Association: Vol 0, No 0</title>
    <dc:date>2020-11-20T19:49:26+00:00</dc:date>
    <link>https://www.tandfonline.com/doi/full/10.1080/01621459.2020.1782219</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Researchers across the health and social sciences generally assume that observations are independent, even while relying on convenience samples that draw subjects from one or a small number of communities, schools, hospitals, etc. A paradigmatic example of this is the Framingham Heart Study (FHS). Many of the limitations of such samples are well-known, but the issue of statistical dependence due to social network ties has not previously been addressed. We show that, along with anticonservative variance estimation, this can result in spurious associations due to network dependence. Using a statistical test that we adapted from one developed for spatial autocorrelation, we test for network dependence in several of the thousands of influential papers that have been published using FHS data. Results suggest that some of the many decades of research on coronary heart disease, other health outcomes, and peer influence using FHS data may suffer from spurious associations, error-prone point estimates, and anticonservative inference due to unacknowledged network dependence. These issues are not unique to the FHS; as researchers in psychology, medicine, and beyond grapple with replication failures, this unacknowledged source of invalid statistical inference should be part of the conversation."]]></description>
<dc:subject>to:NB have_read causal_inference network_data_analysis networks kith_and_kin ogburn.elizabeth to_teach:baby-nets re:homophily_and_confounding social_science_methodology heard_the_talk</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:64e51a725339/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:network_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ogburn.elizabeth"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:baby-nets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:homophily_and_confounding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_science_methodology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:heard_the_talk"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.tandfonline.com/doi/full/10.1080/01621459.2020.1811098">
    <title>Auto-G-Computation of Causal Effects on a Network: Journal of the American Statistical Association: Vol 0, No 0</title>
    <dc:date>2020-11-20T15:33:51+00:00</dc:date>
    <link>https://www.tandfonline.com/doi/full/10.1080/01621459.2020.1811098</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Methods for inferring average causal effects have traditionally relied on two key assumptions: (i) the intervention received by one unit cannot causally influence the outcome of another; and (ii) units can be organized into nonoverlapping groups such that outcomes of units in separate groups are independent. In this article, we develop new statistical methods for causal inference based on a single realization of a network of connected units for which neither assumption (i) nor (ii) holds. The proposed approach allows both for arbitrary forms of interference, whereby the outcome of a unit may depend on interventions received by other units with whom a network path through connected units exists; and long range dependence, whereby outcomes for any two units likewise connected by a path in the network may be dependent. Under network versions of consistency and no unobserved confounding, inference is made tractable by an assumption that the networks outcome, treatment and covariate vectors are a single realization of a certain chain graph model. This assumption allows inferences about various network causal effects via the auto-g-computation algorithm, a network generalization of Robins’ well-known g-computation algorithm previously described for causal inference under assumptions (i) and (ii). Supplementary materials for this article are available online."]]></description>
<dc:subject>to:NB causal_inference statistics network_data_analysis networks re:homophily_and_confounding shpitser.ilya to_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:32a97d68640f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:network_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:homophily_and_confounding"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:shpitser.ilya"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.annualreviews.org/doi/abs/10.1146/annurev-economics-093019-113859">
    <title>Econometric Models of Network Formation | Annual Review of Economics</title>
    <dc:date>2020-11-19T05:14:26+00:00</dc:date>
    <link>https://www.annualreviews.org/doi/abs/10.1146/annurev-economics-093019-113859</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This article provides a selective review of the recent literature on econometric models of network formation. I start with a brief exposition on basic concepts and tools for the statistical description of networks; then I offer a review of dyadic models, focusing on statistical models on pairs of nodes, and I describe several developments of interest to the econometrics literature. I also present a discussion of nondyadic models in which link formation might be influenced by the presence or absence of additional links, which themselves are subject to similar influences. This argument is related to the statistical literature on conditionally specified models and the econometrics of game theoretical models. I close with a (nonexhaustive) discussion of potential areas for further development."]]></description>
<dc:subject>to:NB networks network_data_analysis economics to_teach:baby-nets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2c6c39b0fc04/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:network_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:baby-nets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.annualreviews.org/doi/abs/10.1146/annurev-control-091219-012549">
    <title>Network Effects on the Robustness of Dynamic Systems | Annual Review of Control, Robotics, and Autonomous Systems</title>
    <dc:date>2020-11-18T22:45:23+00:00</dc:date>
    <link>https://www.annualreviews.org/doi/abs/10.1146/annurev-control-091219-012549</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We review selected results related to the robustness of networked systems in finite and asymptotically large size regimes in static and dynamical settings. In the static setting, within the framework of flow over finite networks, we discuss the effect of physical constraints on robustness to loss in link capacities. In the dynamical setting, we review several settings in which small-gain-type analysis provides tight robustness guarantees for linear dynamics over finite networks toward worst-case and stochastic disturbances. We discuss network flow dynamic settings where nonlinear techniques facilitate understanding the effect, on robustness, of constraints on capacity and information, substituting information with control action, and cascading failure. We also contrast cascading failure with a representative contagion model. For asymptotically large networks, we discuss the role of network properties in connecting microscopic shocks to emergent macroscopic fluctuations under linear dynamics as well as for economic networks at equilibrium. Through this review, we aim to achieve two objectives: to highlight selected settings in which the role of the interconnectivity structure of a network in its robustness is well understood, and to highlight a few additional settings in which existing system-theoretic tools give tight robustness guarantees and that are also appropriate avenues for future network-theoretic investigations."]]></description>
<dc:subject>to:NB networks dynamical_systems control_theory_and_control_engineering robustness</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:4debb7827817/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dynamical_systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:control_theory_and_control_engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:robustness"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2011.08148">
    <title>[2011.08148] Causal motifs and existence of endogenous cascades in directed networks with application to company defaults</title>
    <dc:date>2020-11-18T17:18:35+00:00</dc:date>
    <link>https://arxiv.org/abs/2011.08148</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Motivated by detection of cascades of defaults in economy, we developed a detection framework for endogenous spreading based on causal motifs we define in this paper. We assume that vertex change of state can be triggered by endogenous or exogenous event, that underlying network is directed and that times when vertices changed their states are available. In addition to data of company defaults we use, we simulate cascades driven by different stochastic processes on different synthetic networks. We also extended an approximate master equation method to directed networks with temporal stamps in order to understand in which cases detection is possible. We show that some of the smallest motifs can robustly detect cascades."]]></description>
<dc:subject>to:NB contagion networks statistics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d49528813ef6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:contagion"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://projecteuclid.org/euclid.ejs/1575946867">
    <title>Krampe : Time series modeling on dynamic networks</title>
    <dc:date>2020-11-16T16:14:17+00:00</dc:date>
    <link>https://projecteuclid.org/euclid.ejs/1575946867</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This paper focuses on modeling the dynamic attributes of a dynamic network with a fixed number of vertices. These attributes are considered as time series which dependency structure is influenced by the underlying network. They are modeled by a multivariate doubly stochastic time series framework, that is we assume linear processes for which the coefficient matrices are stochastic processes themselves. We explicitly allow for dependence in the dynamics of the coefficient matrices as well as between the two stochastic processes driving the time series. This framework allows for a separate modeling of the attributes and the underlying network. In this setting, we define network autoregressive models and discuss their stationarity conditions. Furthermore, an estimation approach is discussed in a low- and high-dimensional setting and how this can be applied to forecasting. The finite sample behavior of the forecast approach is investigated. This approach is applied to real data whereby the goal is to forecast the GDP of 33 economies."]]></description>
<dc:subject>to:NB time_series statistics networks network_data_analysis</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a953bb4a45b6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:time_series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:network_data_analysis"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://projecteuclid.org/euclid.ejs/1576119708">
    <title>Zheng , Raskutti : Testing for high-dimensional network parameters in auto-regressive models</title>
    <dc:date>2020-11-16T16:13:38+00:00</dc:date>
    <link>https://projecteuclid.org/euclid.ejs/1576119708</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["High-dimensional auto-regressive models provide a natural way to model influence between MM actors given multi-variate time series data for TT time intervals. While there has been considerable work on network estimation, there is limited work in the context of inference and hypothesis testing. In particular, prior work on hypothesis testing in time series has been restricted to linear Gaussian auto-regressive models. From a practical perspective, it is important to determine suitable statistical tests for connections between actors that go beyond the Gaussian assumption. In the context of high-dimensional time series models, confidence intervals present additional estimators since most estimators such as the Lasso and Dantzig selectors are biased which has led to de-biased estimators. In this paper we address these challenges and provide convergence in distribution results and confidence intervals for the multi-variate AR(p) model with sub-Gaussian noise, a generalization of Gaussian noise that broadens applicability and presents numerous technical challenges. The main technical challenge lies in the fact that unlike Gaussian random vectors, for sub-Gaussian vectors zero correlation does not imply independence. The proof relies on using an intricate truncation argument to develop novel concentration bounds for quadratic forms of dependent sub-Gaussian random variables. Our convergence in distribution results hold provided T=Ω((s∨ρ)2log2M)T=Ω((s∨ρ)2log2⁡M), where ss and ρρ refer to sparsity parameters which matches existed results for hypothesis testing with i.i.d. samples. We validate our theoretical results with simulation results for both block-structured and chain-structured networks."]]></description>
<dc:subject>to:NB high-dimensional_statistics time_series networks network_data_analysis statistics to_teach:data_over_space_and_time</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:8ca5e6167496/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:high-dimensional_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:time_series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:network_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data_over_space_and_time"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2005.04751">
    <title>[2005.04751] Tractable nonlinear memory functions as a tool to capture and explain dynamical behaviours</title>
    <dc:date>2020-11-15T21:15:24+00:00</dc:date>
    <link>https://arxiv.org/abs/2005.04751</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Mathematical approaches from dynamical systems theory are used in a range of fields. This includes biology where they are used to describe processes such as protein-protein interaction and gene regulatory networks. As such networks increase in size and complexity, detailed dynamical models become cumbersome, making them difficult to explore and decipher. This necessitates the application of simplifying and coarse graining techniques in order to derive explanatory insight. Here we demonstrate that Zwanzig-Mori projection methods can be used to arbitrarily reduce the dimensionality of dynamical networks while retaining their dynamical properties. We show that a systematic expansion around the quasi-steady state approximation allows an explicit solution for memory functions without prior knowledge of the dynamics. The approach not only preserves the same steady states but also replicates the transients of the original system. The method also correctly predicts the dynamics of multistable systems as well as networks producing sustained and damped oscillations. Applying the approach to a gene regulatory network from the vertebrate neural tube, a well characterised developmental transcriptional network, identifies features of the regulatory network responsible dfor its characteristic transient behaviour. Taken together, our analysis shows that this method is broadly applicable to multistable dynamical systems and offers a powerful and efficient approach for understanding their behaviour."]]></description>
<dc:subject>to:NB dynamical_systems approximation networks biochemical_networks non-equilibrium</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3dff9992307a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dynamical_systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:approximation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:non-equilibrium"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2006.03286">
    <title>[2006.03286] Opportunities at the interface of network science and metabolic modelling</title>
    <dc:date>2020-11-15T21:14:47+00:00</dc:date>
    <link>https://arxiv.org/abs/2006.03286</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Metabolism plays a central role in cell physiology as it provides the molecular machinery for growth. At the genome-scale, metabolism is made up of thousands of reactions interacting with one another. Untangling this complexity is key to understand how cells respond to genetic, environmental, or therapeutic perturbations. Here we discuss the roles of two complementary strategies for the analysis of genome-scale metabolic models: Flux Balance Analysis (FBA) and network science. While FBA estimates metabolic flux on the basis of an optimisation principle, network approaches reveal emergent properties of the global metabolic connectivity. We highlight how the integration of both approaches promises to deliver insights on the structure and function of metabolic systems with wide-ranging implications in discovery science, precision medicine and industrial biotechnology."]]></description>
<dc:subject>to:NB biochemical_networks networks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:4edb0b5767ec/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:biochemical_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2010.00613">
    <title>[2010.00613] Unified Treatment of Dynamical Processes on Generalized Networks: Higher-Order, Multilayer, and Temporal Interactions</title>
    <dc:date>2020-10-23T19:31:14+00:00</dc:date>
    <link>https://arxiv.org/abs/2010.00613</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["When describing complex interconnected systems, one often has to go beyond the traditional network description to account for generalized interactions. Here, we establish a unified framework to optimally simplify the analysis of cluster synchronization patterns for a wide range of generalized networks, including hypergraphs, multilayer networks, and temporal networks. The framework is based on finding the finest simultaneous block diagonalization (SBD) of the matrices encoding the synchronization pattern and the interaction pattern. As an application, we use the SBD framework to characterize chimera states induced by nonpairwise interactions and by time-varying interactions. The unified framework established here can be extended to other dynamical processes and can facilitate the discovery of novel emergent phenomena in complex systems with generalized interactions."]]></description>
<dc:subject>to:NB networks dynamical_systems to_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:1c139585a828/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dynamical_systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://link.springer.com/article/10.1007/s41109-020-00301-2">
    <title>The multiplex nature of global financial contagions | SpringerLink</title>
    <dc:date>2020-10-23T16:59:35+00:00</dc:date>
    <link>https://link.springer.com/article/10.1007/s41109-020-00301-2</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["As illustrated by the 2008 global financial crisis, the financial distress of one country can trigger financial distress in other countries. We examine the problem of identifying such “systemically important” countries (i.e., countries whose financial distress can trigger further distress), which is important for assessing global financial stability. Using data on bilateral financial positions that are split by asset type, we build a multiplex global financial network in which nodes represent countries, edges encode cross-country financial assets of various types, and layers represent asset types. We examine the temporal evolution of a measure of node importance known as MultiRank centrality, and we find that several major European countries decrease in rank and that several major Asian countries increase in rank since 2008. We then develop a multiplex threshold model of financial contagions in which a shock can propagate either within a layer or between layers. We find that the number of systemically important countries can be twice as large when we take into account the heterogeneity of financial exposures (i.e., when using a multiplex network) than in a contagion on an associated aggregate global financial network (i.e., on a monolayer network), as is often examined in other studies. We also study the extent to which buffers can reduce the propagation of financial distress. Our analysis suggests that accounting for both intralayer and interlayer propagation of contagions in a multiplex structure of financial assets is important for understanding interconnected financial systems of countries."]]></description>
<dc:subject>to:NB finance contagion network_data_analysis networks porter.mason_a.</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:e9f240e1a070/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:finance"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:contagion"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:network_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:porter.mason_a."/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://bengolub.net/papers/SNFF.pdf">
    <title>Supply Network Formation and Fragility</title>
    <dc:date>2020-04-08T14:11:38+00:00</dc:date>
    <link>http://bengolub.net/papers/SNFF.pdf</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We model the production of complex goods in a large supply network. Firms source
several essential inputs through relationships with other firms. Due to the risk of such supply
relationships being idiosyncratically disrupted, firms multisource inputs and invest to make relationships with suppliers stronger. In equilibrium, aggregate production is robust to idiosyncratic
disruptions. However, depending on parameters, the supply network may be robust or arbitrarily
sensitive to small aggregate shocks that affect the functioning of relationships. We give conditions
under which the equilibrium network is driven to a fragile configuration, where arbitrarily small
aggregate shocks cause discontinuous losses. We use the model to provide a unified account of a
number of stylized facts about complex economies."

--- I could swear that Krugman had basically this model in a page of _The Self-organizing Economy_ (1996), only my copy is locked in my campus office...]]></description>
<dc:subject>to:NB percolation_theory networks economics macroeconomics to_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:5dfe92f1bb3e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:percolation_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macroeconomics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://press.uchicago.edu/ucp/books/book/chicago/P/bo48408506">
    <title>Power in Modernity: Agency Relations and the Creative Destruction of the King’s Two Bodies, Reed</title>
    <dc:date>2020-04-03T04:13:43+00:00</dc:date>
    <link>https://press.uchicago.edu/ucp/books/book/chicago/P/bo48408506</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In Power in Modernity, Isaac Ariail Reed proposes a bold new theory of power that describes overlapping networks of delegation and domination.  Chains of power and their representation, linking together groups and individuals across time and space, create a vast network of intersecting alliances, subordinations, redistributions, and violent exclusions. Reed traces the common action of “sending someone else to do something for you” as it expands outward into the hierarchies that control territories, persons, artifacts, minds, and money.
"He mobilizes this theory to investigate the onset of modernity in the Atlantic world, with a focus on rebellion, revolution, and state formation in colonial North America, the early American Republic, the English Civil War, and French Revolution. Modernity, Reed argues, dismantled the “King’s Two Bodies”—the monarch’s physical body and his ethereal, sacred second body that encompassed the body politic—as a schema of representation for forging power relations. Reed’s account then offers a new understanding of the democratic possibilities and violent exclusions forged in the name of “the people,” as revolutionaries sought new ways to secure delegation, build hierarchy, and attack alterity.
"Reconsidering the role of myth in modern politics, Reed proposes to see the creative destruction and eternal recurrence of the King’s Two Bodies as constitutive of the modern attitude, and thus as a new starting point for critical theory. Modernity poses in a new way an eternal human question: what does it mean to be the author of one’s own actions?"]]></description>
<dc:subject>to:NB books:noted networks modernity barely-comprehensible_metaphysics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:04333d593589/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:modernity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:barely-comprehensible_metaphysics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/cond-mat/0205439">
    <title>[cond-mat/0205439] Epidemic threshold in structured scale-free networks</title>
    <dc:date>2020-02-15T22:35:17+00:00</dc:date>
    <link>https://arxiv.org/abs/cond-mat/0205439</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We analyze the spreading of viruses in scale-free networks with high clustering and degree correlations, as found in the Internet graph. For the Suscetible-Infected-Susceptible model of epidemics the prevalence undergoes a phase transition at a finite threshold of the transmission probability. Comparing with the absence of a finite threshold in networks with purely random wiring, our result suggests that high clustering and degree correlations protect scale-free networks against the spreading of viruses. We introduce and verify a quantitative description of the epidemic threshold based on the connectivity of the neighborhoods of the hubs."

--- Initially, I found the focus on the average degree of a node's neighbors, their <k^nn>, very puzzling --- as a measure of how many secondary infections you could produce, this would seem to involve a lot of over-counting when the network is clustered.  But looking at their figure 2 clarifies: <k^nn|k>, the average degree of neighbors conditional on ego's degree, is a _decreasing_ function of ego's degree in their model.  (If ego's degree is 1 or 2, it looks like the average degree of ego's neighbors is in the 100s [!], while as ego's degree goes to infinity, <k^nn> tends to a constant _smaller_ than the average degree.)  So this is a hub-and-spoke system where each hub has a huge number of ties to very low-degree nodes, but there are enough non-hubs tied to multiple hubs, or hub-hub ties, to keep things connected.  And then it makes sense that the crucial step in an epidemic is what happens once a hub is infected.

ETA: In fact, Moreno and Vazquez (cond-mat/0210362) observe that the model generates, basically, a linear chain of stars (lovely phrase!), and that's where all this weird behavior comes from.

(Despite the last tag, I think this model is so anti-social that it's not worth mentioning in the paper with DA and HF, but maybe if I ever write that review...)]]></description>
<dc:subject>in_NB have_read epidemics_on_networks networks re:do-institutions-evolve</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f26bb1306d9e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:epidemics_on_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/cond-mat/0007048">
    <title>[cond-mat/0007048] Resilience of the Internet to random breakdowns</title>
    <dc:date>2020-02-15T20:36:46+00:00</dc:date>
    <link>https://arxiv.org/abs/cond-mat/0007048</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["A common property of many large networks, including the Internet, is that the connectivity of the various nodes follows a scale-free power-law distribution, P(k)=ck^-a. We study the stability of such networks with respect to crashes, such as random removal of sites. Our approach, based on percolation theory, leads to a general condition for the critical fraction of nodes, p_c, that need to be removed before the network disintegrates. We show that for a<=3 the transition never takes place, unless the network is finite. In the special case of the Internet (a=2.5), we find that it is impressively robust, where p_c is approximately 0.99."]]></description>
<dc:subject>in_NB networks have_read re:do-institutions-evolve</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:bbc609f42a6e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/cond-mat/0107066">
    <title>[cond-mat/0107066] Immunization of complex networks</title>
    <dc:date>2020-02-15T20:06:23+00:00</dc:date>
    <link>https://arxiv.org/abs/cond-mat/0107066</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Complex networks such as the sexual partnership web or the Internet often show a high degree of redundancy and heterogeneity in their connectivity properties. This peculiar connectivity provides an ideal environment for the spreading of infective agents. Here we show that the random uniform immunization of individuals does not lead to the eradication of infections in all complex networks. Namely, networks with scale-free properties do not acquire global immunity from major epidemic outbreaks even in the presence of unrealistically high densities of randomly immunized individuals. The absence of any critical immunization threshold is due to the unbounded connectivity fluctuations of scale-free networks. Successful immunization strategies can be developed only by taking into account the inhomogeneous connectivity properties of scale-free networks. In particular, targeted immunization schemes, based on the nodes' connectivity hierarchy, sharply lower the network's vulnerability to epidemic attacks."]]></description>
<dc:subject>in_NB have_read networks epidemics_on_networks re:do-institutions-evolve</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:eb8d09e252a9/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:epidemics_on_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://iopscience.iop.org/article/10.1088/1742-5468/2013/12/P12002">
    <title>Spreading dynamics in complex networks - IOPscience</title>
    <dc:date>2020-02-15T19:22:54+00:00</dc:date>
    <link>https://iopscience.iop.org/article/10.1088/1742-5468/2013/12/P12002</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Searching for influential spreaders in complex networks is an issue of great significance for applications across various domains, ranging from epidemic control, innovation diffusion, viral marketing, and social movement to idea propagation. In this paper, we first display some of the most important theoretical models that describe spreading processes, and then discuss the problem of locating both the individual and multiple influential spreaders respectively. Recent approaches in these two topics are presented. For the identification of privileged single spreaders, we summarize several widely used centralities, such as degree, betweenness centrality, PageRank, k-shell, etc. We investigate the empirical diffusion data in a large scale online social community—LiveJournal. With this extensive dataset, we find that various measures can convey very distinct information of nodes. Of all the users in the LiveJournal social network, only a small fraction of them are involved in spreading. For the spreading processes in LiveJournal, while degree can locate nodes participating in information diffusion with higher probability, k-shell is more effective in finding nodes with a large influence. Our results should provide useful information for designing efficient spreading strategies in reality."

--- Eh, the measure of "influence" is just the size of the reachable set.  (They don't actually track the dynamics of anything.)]]></description>
<dc:subject>networks re:do-institutions-evolve social_influence have_read in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ebffd8b03897/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.nature.com/articles/s41598-018-31902-8">
    <title>A comparative analysis of approaches to network-dismantling | Scientific Reports</title>
    <dc:date>2020-02-15T18:37:45+00:00</dc:date>
    <link>https://www.nature.com/articles/s41598-018-31902-8</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Estimating, understanding, and improving the robustness of networks has many application areas such as bioinformatics, transportation, or computational linguistics. Accordingly, with the rise of network science for modeling complex systems, many methods for robustness estimation and network dismantling have been developed and applied to real-world problems. The state-of-the-art in this field is quite fuzzy, as results are published in various domain-specific venues and using different datasets. In this study, we report, to the best of our knowledge, on the analysis of the largest benchmark regarding network dismantling. We reimplemented and compared 13 competitors on 12 types of random networks, including ER, BA, and WS, with different network generation parameters. We find that network metrics, proposed more than 20 years ago, are often non-dominating competitors, while many recently proposed techniques perform well only on specific network types. Besides the solution quality, we also investigate the execution time. Moreover, we analyze the similarity of competitors, as induced by their node rankings. We compare and validate our results on real-world networks. Our study is aimed to be a reference for selecting a network dismantling method for a given network, considering accuracy requirements and run time constraints."]]></description>
<dc:subject>networks re:do-institutions-evolve have_read in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ec16d273beed/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:do-institutions-evolve"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1909.05416">
    <title>[1909.05416] Cascade Size Distributions and Why They Matter</title>
    <dc:date>2019-10-29T21:58:28+00:00</dc:date>
    <link>https://arxiv.org/abs/1909.05416</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["How likely is it that a few initial node activations are amplified to produce large response cascades that span a considerable part of an entire network? Our answer to this question relies on the Independent Cascade Model for weighted directed networks. In using this model, most of our insights have been derived from the study of average effects. Here, we shift the focus on the full probability distribution of the final cascade size. This shift allows us to explore both typical cascade outcomes and improbable but relevant extreme events. We present an efficient message passing algorithm to compute the final cascade size distribution and activation probabilities of nodes conditional on the final cascade size. Our approach is exact on trees but can be applied to any network topology. It approximates locally tree-like networks well and can lead to surprisingly good performance on more dense networks, as we show using real world data, including a miRNA- miRNA probabilistic interaction network for gastrointestinal cancer. We demonstrate the utility of our algorithms for clustering of nodes according to their functionality and influence maximization."]]></description>
<dc:subject>to:NB branching_processes networks epidemics_on_networks information_cascades</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:118b88749499/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:branching_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:epidemics_on_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_cascades"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1910.09538">
    <title>[1910.09538] The Transsortative Structure of Networks</title>
    <dc:date>2019-10-22T13:22:33+00:00</dc:date>
    <link>https://arxiv.org/abs/1910.09538</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Network topologies can be non-trivial, due to the complex underlying behaviors that form them. While past research has shown that some processes on networks may be characterized by low-order statistics describing nodes and their neighbors, such as degree assortativity, these quantities fail to capture important sources of variation in network structure. We introduce a property called transsortativity that describes correlations among a node's neighbors, generalizing these statistics from immediate one-hop neighbors to two-hop neighbors. We describe how transsortativity can be systematically varied, independently of the network's degree distribution and assortativity. Moreover, we show that it can significantly impact the spread of contagions as well as the perceptions of neighbors, known as the majority illusion. Our work improves our ability to create and analyze more realistic models of complex networks."]]></description>
<dc:subject>to:NB networks network_data_analysis lerman.kristina to_read to_teach:baby-nets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:cccb829241ac/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:network_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:lerman.kristina"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:baby-nets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1909.09239">
    <title>[1909.09239] Systemic Cascades On Inhomogeneous Random Financial Networks</title>
    <dc:date>2019-09-23T14:15:53+00:00</dc:date>
    <link>https://arxiv.org/abs/1909.09239</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This systemic risk paper introduces inhomogeneous random financial networks (IRFNs). Such models are intended to describe parts, or the entirety, of a highly heterogeneous network of banks and their interconnections, in the global financial system. Both the balance sheets and the stylized crisis behaviour of banks are ingredients of the network model. A systemic crisis is pictured as triggered by a shock to banks' balance sheets, which then leads to the propagation of damaging shocks and the potential for amplification of the crisis, ending with the system in a cascade equilibrium. Under some conditions the model has ``locally tree-like independence (LTI)'', where a general percolation theoretic argument leads to an analytic fixed point equation describing the cascade equilibrium when the number of banks N in the system is taken to infinity. This paper focusses on mathematical properties of the framework in the context of Eisenberg-Noe solvency cascades generalized to account for fractional bankruptcy charges. New results including a definition and proof of the ``LTI property'' of the Eisenberg-Noe solvency cascade mechanism lead to explicit N=∞ fixed point equations that arise under very general model specifications. The essential formulas are shown to be implementable via well-defined approximation schemes, but numerical exploration of some of the wide range of potential applications of the method is left for future work."]]></description>
<dc:subject>to:NB networks financial_markets</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:fa2dcc343ac0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:financial_markets"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1909.04843">
    <title>[1909.04843] Clusters and the entropy in opinion dynamics on complex networks</title>
    <dc:date>2019-09-15T17:24:54+00:00</dc:date>
    <link>https://arxiv.org/abs/1909.04843</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In this work, we investigate a heterogeneous population in the modified Hegselmann-Krause opinion model on complex networks. We introduce the Shannon information entropy about all relative opinion clusters to characterize the cluster profile in the final configuration. Independent of network structures, there exists the optimal stubbornness of one subpopulation for the largest number of clusters and the highest entropy. Besides, there is the optimal bounded confidence (or subpopulation ratio) of one subpopulation for the smallest number of clusters and the lowest entropy. However, network structures affect cluster profiles indeed. A large average degree favors consensus for making different networks more similar with complete graphs. The network size has limited impact on cluster profiles of heterogeneous populations on scale-free networks but has significant effects upon those on small-world networks."]]></description>
<dc:subject>to:NB networks voter_model</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:34088d8bb822/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:voter_model"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1909.01288">
    <title>[1909.01288] Irrelevance of linear controllability to nonlinear dynamical networks</title>
    <dc:date>2019-09-04T15:16:36+00:00</dc:date>
    <link>https://arxiv.org/abs/1909.01288</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["There has been tremendous development of linear controllability of complex networks. Real-world systems are fundamentally nonlinear. Is linear controllability relevant to nonlinear dynamical networks? We identify a common trait underlying both types of control: the nodal "importance." For nonlinear and linear control, the importance is determined, respectively, by physical/biological considerations and the probability for a node to be in the minimum driver set. We study empirical mutualistic networks and a gene regulatory network, for which the nonlinear nodal importance can be quantified by the ability of individual nodes to restore the system from the aftermath of a tipping-point transition. We find that the nodal importance ranking for nonlinear and linear control exhibits opposite trends: for the former large-degree nodes are more important but for the latter, the importance scale is tilted towards the small-degree nodes, suggesting strongly irrelevance of linear controllability to these systems. The recent claim of successful application of linear controllability to C. elegans connectome is examined and discussed."]]></description>
<dc:subject>to:NB control_theory_and_control_engineering dynamical_systems networks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:423609a2b5de/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:control_theory_and_control_engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dynamical_systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1908.07092">
    <title>[1908.07092] Spectral theory for the stability of dynamical systems on large oriented locally tree-like graphs</title>
    <dc:date>2019-08-21T13:24:35+00:00</dc:date>
    <link>https://arxiv.org/abs/1908.07092</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We develop a mathematical theory for the linear stability of stationary states in large dynamical systems modelled by a set of randomly coupled differential equations on a locally tree-like network. Our approach provides analytical expressions for the leading eigenvalue of random matrices that describe the interactions between the degrees of freedom; the sign of the leading eigenvalue characterizes the system stability. We illustrate this approach on oriented random graphs with a prescribed degree distribution and find that the leading eigenvalue is universal in the sense that it only depends on a few ensemble parameters, including the mean degree and a degree correlation coefficient. In addition, we also characterize the unstable mode of the system of interest by deriving analytical expressions for the statistics of the components of the right and left eigenvectors associated with the leading eigenvalue. Finally, we briefly discuss how this approach can be extended to models with diagonal disorder and non-oriented couplings."]]></description>
<dc:subject>to:NB dynamical_systems networks dynamics_on_networks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a2121c0577a5/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dynamical_systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dynamics_on_networks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1908.07025">
    <title>[1908.07025] Digraphs are different: Why directionality matters in complex systems</title>
    <dc:date>2019-08-21T13:19:33+00:00</dc:date>
    <link>https://arxiv.org/abs/1908.07025</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Many networks describing complex systems are directed: the interactions between elements are not symmetric. Recent work has shown that these networks can display properties such as trophic coherence or non-normality, which in turn affect stability, percolation and other dynamical features. I show here that these topological properties have a common origin, in that the edges of directed networks can be aligned - or not - with a global direction. And I illustrate how this can lead to rich and unexpected dynamical behaviour even in the simplest of models."]]></description>
<dc:subject>to:NB networks dynamics_on_networks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ee9add260f98/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dynamics_on_networks"/>
</rdf:Bag></taxo:topics>
</item>
</rdf:RDF>