Efficiency of attack strategies on complex model and real-world networks

Michele Bellingeri, Davide Cassi and Simone Vincenzi

Physica A: Statistical Mechanics and its Applications, 2014, vol. 414, issue C, 174-180

Abstract: We investigated the efficiency of attack strategies to network nodes when targeting several complex model and real-world networks. We tested 5 attack strategies, 3 of which were introduced in this work for the first time, to attack 3 model networks (Erdos and Renyi, Barabasi and Albert preferential attachment network, and scale-free network configuration models) and 3 real networks (Gnutella peer-to-peer network, email network of the University of Rovira i Virgili, and immunoglobulin interaction network). Nodes were removed sequentially according to the importance criterion defined by the attack strategy, and we used the size of the largest connected component (LCC) as a measure of network damage. We found that the efficiency of attack strategies (fraction of nodes to be deleted for a given reduction of LCC size) depends on the topology of the network, although attacks based on either the number of connections of a node or betweenness centrality were often the most efficient strategies. Sequential deletion of nodes in decreasing order of betweenness centrality was the most efficient attack strategy when targeting real-world networks. The relative efficiency of attack strategies often changed during the sequential removal of nodes, especially for networks with power-law degree distribution.

Keywords: Network robustness; Attack strategies; Immunization strategies (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (23)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:414:y:2014:i:c:p:174-180

DOI: 10.1016/j.physa.2014.06.079

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