Supracentrality Analysis of Temporal Networks with Directed Interlayer Coupling

Taylor, Dane; Porter, Mason A.; Mucha, Peter J.

doi:10.1007/978-3-030-23495-9_17

Cited by 15 publications

(24 citation statements)

References 70 publications

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“…This would enable comparative validation of different modeling frameworks. Studies of the relationship between measures of time-dependent centralities (38,46,47) and dynamic models of hierarchy would also be valuable. In particular, the theory of time-dependent centralities faces an important methodological issue: Different reasonable ranking methods can yield directionally different orderings of nodes when applied to the same dataset (48).…”

Section: Discussionmentioning

confidence: 99%

Emergence of hierarchy in networked endorsement dynamics

Kawakatsu

Chodrow

Eikmeier

et al. 2021

Proc. Natl. Acad. Sci. U.S.A.

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Many social and biological systems are characterized by enduring hierarchies, including those organized around prestige in academia, dominance in animal groups, and desirability in online dating. Despite their ubiquity, the general mechanisms that explain the creation and endurance of such hierarchies are not well understood. We introduce a generative model for the dynamics of hierarchies using time-varying networks, in which new links are formed based on the preferences of nodes in the current network and old links are forgotten over time. The model produces a range of hierarchical structures, ranging from egalitarianism to bistable hierarchies, and we derive critical points that separate these regimes in the limit of long system memory. Importantly, our model supports statistical inference, allowing for a principled comparison of generative mechanisms using data. We apply the model to study hierarchical structures in empirical data on hiring patterns among mathematicians, dominance relations among parakeets, and friendships among members of a fraternity, observing several persistent patterns as well as interpretable differences in the generative mechanisms favored by each. Our work contributes to the growing literature on statistically grounded models of time-varying networks.

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Section: Discussionmentioning

confidence: 99%

Emergence of hierarchy in networked endorsement dynamics

Kawakatsu

Chodrow

Eikmeier

et al. 2021

Proc. Natl. Acad. Sci. U.S.A.

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“…e.g. [13,14,37,43,[51][52][53]. These allow entities to interact in several different ways or reflect changing relationships over time leading to ever more realistic models of highly complex phenomena.…”

Section: Introductionmentioning

confidence: 99%

“…The formal representation of multilayer networks is a non-trivial task [37] and for this paper we choose the representation in terms of a supra-adjacency matrix. The same representation has already been successfully used to generalize eigenvector centralities to the case of temporal and multiplex networks [51][52][53]. Other approaches for generalizing eigenvector centrality to the multilayer case include using the network's representation in terms of an adjacency tensor and computing its largest eigentensor [54,56].…”

Section: Introductionmentioning

confidence: 99%

Fast computation of matrix function-based centrality measures for layer-coupled multiplex networks

Bergermann,

Stoll

2021

Preprint

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Centrality measures identify the most important nodes in a complex network. In recent years, multilayer networks have emerged as a flexible tool to create increasingly realistic models of complex systems. In this paper, we generalize matrix function-based centrality and communicability measures to the case of layer-coupled multiplex networks. We use the supra-adjacency matrix as the network representation, which has already been used to generalize eigenvector centrality to temporal and multiplex networks. With this representation, the definition of single-layer matrix function-based centrality measures in terms of walks on the networks carries over naturally to the multilayer case. Several aggregation techniques allow the ranking of nodes, layers, as well as node-layer pairs in terms of their importance in the network. We present efficient and scalable numerical methods based on Krylov subspace techniques and Gauss quadrature rules, which provide a high accuracy in only a few iterations and which scale linearly in the network size under the assumption of sparsity in the supra-adjacency matrix. Finally, we present extensive numerical studies for both directed and undirected as well as weighted and unweighted multiplex networks. While we focus on social and transportation applications the networks' size ranges between 89 and 2.28 • 10 6 nodes and between 3 and 37 layers.

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“…In the context of our work, an essential class of tools for the analysis of temporal networks are centrality measures [21]. The approach to their design varies greatly, ranging from the analysis of network flows [22] and shortest temporal paths [23], to the applications of eigenvector-like techniques [24], [25], [26].…”

Section: Related Workmentioning

confidence: 99%

Hiding in Temporal Networks

Waniek¹,

Holme

Rahwan³

2022

IEEE Trans. Netw. Sci. Eng.

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Social network analysis tools can infer various attributes just by scrutinizing one's connections. Several researchers have studied the problem faced by an evader whose goal is to strategically rewire their social connections in order to mislead such tools, thereby concealing their private attributes. However, to date this literature has only considered static networks, while neglecting the more general case of temporal networks, where the structure evolves over time. Driven by this observation, we study how the evader can conceal their importance from an adversary armed with temporal centrality measures. We consider computational and structural aspects of this problem: Is it computationally feasible to calculate optimal ways of hiding? If it is, what network characteristics facilitate hiding? This topic has been studied in static networks, but in this work, we add realism to the problem by considering temporal networks of edges changing in time. We find that it is usually computationally infeasible to find the optimal way of hiding. On the other hand, by manipulating one's contacts, one could add a surprising amount of privacy. Compared to static networks, temporal networks offer more strategies for this type of manipulation and are thus, to some extent, easier to hide in.

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Supracentrality Analysis of Temporal Networks with Directed Interlayer Coupling

Cited by 15 publications

References 70 publications

Emergence of hierarchy in networked endorsement dynamics

Emergence of hierarchy in networked endorsement dynamics

Fast computation of matrix function-based centrality measures for layer-coupled multiplex networks

Hiding in Temporal Networks

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