Eigenvector Centrality Measure Based on Node Similarity for Multilayer and Temporal Networks

Lv, Laishui; Zhang, Kun; Zhang, Ting; Li, Xun; Zhang, Jiahui; Xue, Wei

doi:10.1109/access.2019.2936217

Cited by 19 publications

(12 citation statements)

References 37 publications

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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%

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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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Section: Related Workmentioning

confidence: 99%

“…• Eigenvector temporal centrality [25]-importance of a node v corresponds to the importance of its neighbors. The temporal version of the eigenvector centrality was defined in a number of ways [24], [37], [38], [39].…”

Section: Temporal Centrality Measuresmentioning

confidence: 99%

Hiding in Temporal Networks

Waniek¹,

Holme

Rahwan³

2022

IEEE Trans. Netw. Sci. Eng.

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“…In the context of our work, an essential class of tools for the analysis of temporal networks are centrality measures [23]. The approach to their design varies greatly, ranging from the analysis of network flows [42] and shortest temporal paths [34], to the applications of eigenvector-like techniques [43,26,44].…”

Section: Related Workmentioning

confidence: 99%

“…The temporal version of the eigenvector centrality was defined in a number of ways [43,17,55,45]. In our work we use algorithm by Lv et al [26], as it allows to efficiently process even relatively large networks.…”

Section: Temporal Centrality Measuresmentioning

confidence: 99%

Hiding in Multilayer Networks

Waniek

Michalak

Rahwan

2020

AAAI

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Multilayer networks allow for modeling complex relationships, where individuals are embedded in multiple social networks at the same time. Given the ubiquity of such relationships, these networks have been increasingly gaining attention in the literature. This paper presents the first analysis of the robustness of centrality measures against strategic manipulation in multilayer networks. More specifically, we consider an “evader” who strategically chooses which connections to form in a multilayer network in order to obtain a low centrality-based ranking—thereby reducing the chance of being highlighted as a key figure in the network—while ensuring that she remains connected to a certain group of people. We prove that determining an optimal way to “hide” is NP-complete and hard to approximate for most centrality measures considered in our study. Moreover, we empirically evaluate a number of heuristics that the evader can use. Our results suggest that the centrality measures that are functions of the entire network topology are more robust to such a strategic evader than their counterparts which consider each layer separately.

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“…The study of centrality measures on large networks therefore occupies a central place in network theory, with hundreds of measures defined for general or specific purposes (Jalili et al 2015;Lü et al 2016). Such measures have been extended to networks with additional structure, such as modularity (Ghalmane et al 2019) or multiple interconnected layers (so-called mutiplex or multilayer networks) (Taylor et al 2019;Lv et al 2019;Pedroche et al 2016;Agryzkov et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Dynamic centrality measures for cattle trade networks

2021

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We study network centrality measures that take into account the specific structure of networks with time-stamped edges. In particular, we explore how such measures can be used to identify nodes most relevant for the spread of epidemics on directed, temporal contact networks. We present a percolation study on the French cattle trade network, proving that time-aware centrality measures such as the TempoRank significantly outperform measures defined on the static network. In order to make TempoRank amenable to large-scale networks, we show how it can be efficiently computed through direct simulation of time-respecting random walks.

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Eigenvector Centrality Measure Based on Node Similarity for Multilayer and Temporal Networks

Cited by 19 publications

References 37 publications

Hiding in Temporal Networks

Hiding in Temporal Networks

Hiding in Multilayer Networks

Dynamic centrality measures for cattle trade networks

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