TIME CENTRALITY IN DYNAMIC COMPLEX NETWORKS

Eduardo C. Costa, Alex B. Vieira (), Klaus Wehmuth (), Artur Ziviani and Ana Paula Couto Da Silva ()
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Eduardo C. Costa: Computer Science Department, Universidade Federal de Juiz de Fora (UFJF), 36036-330 Juiz de Fora, MG, Brazil†Computer Science Department, National Laboratory for Scientific Computing (LNCC), 25651-075, Petrópolis, RJ, Brazil
Alex B. Vieira: Computer Science Department, Universidade Federal de Juiz de Fora (UFJF), 36036-330 Juiz de Fora, MG, Brazil
Klaus Wehmuth: #x2020;Computer Science Department, National Laboratory for Scientific Computing (LNCC), 25651-075, Petrópolis, RJ, Brazil
Artur Ziviani: #x2020;Computer Science Department, National Laboratory for Scientific Computing (LNCC), 25651-075, Petrópolis, RJ, Brazil
Ana Paula Couto Da Silva: #x2021;Computer Science Department, Universidade Federal de Minas Gerais (UFMG), 31270-010 Belo Horizonte, MG, Brazil

Advances in Complex Systems (ACS), 2015, vol. 18, issue 07n08, 1-14

Abstract: There is an ever-increasing interest in investigating dynamics in time-varying graphs (TVGs). Nevertheless, so far, the notion of centrality in TVG scenarios usually refers to metrics that assess the relative importance of nodes along the temporal evolution of the dynamic complex network. For some TVG scenarios, however, more important than identifying the central nodes under a given node centrality definition is identifying the key time instants for taking certain actions. In this paper, we thus introduce and investigate the notion of time centrality in TVGs. Analogously to node centrality, time centrality evaluates the relative importance of time instants in dynamic complex networks. In this context, we present two time centrality metrics related to diffusion processes. We evaluate the two defined metrics using both a real-world dataset representing an in-person contact dynamic network and a synthetically generated randomized TVG. We validate the concept of time centrality showing that diffusion starting at the best ranked time instants (i.e., the most central ones), according to our metrics, can perform a faster and more efficient diffusion process.

Keywords: Time-varying graphs; diffusion process; temporal networks (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1142/S021952591550023X

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