Attack tolerance of correlated time-varying social networks with well-defined communities

Souvik Sur, Niloy Ganguly and Animesh Mukherjee

Physica A: Statistical Mechanics and its Applications, 2015, vol. 420, issue C, 98-107

Abstract: In this paper, we investigate the efficiency and the robustness of information transmission for real-world social networks, modeled as time-varying instances, under targeted attack in shorter time spans. We observe that these quantities are markedly higher than that of the randomized versions of the considered networks. An important factor that drives this efficiency or robustness is the presence of short-time correlations across the network instances which we quantify by a novel metric the—edge emergence factor, denoted as ξ. We find that standard targeted attacks are not effective in collapsing this network structure. Remarkably, if the hourly community structures of the temporal network instances are attacked with the largest size community attacked first, the second largest next and so on, the network soon collapses. This behavior, we show is an outcome of the fact that the edge emergence factor bears a strong positive correlation with the size ordered community structures.

Keywords: Time-varying networks; Efficiency; Robustness; Attack tolerance; Community analysis (search for similar items in EconPapers)
Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:420:y:2015:i:c:p:98-107

DOI: 10.1016/j.physa.2014.08.074

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