Abstract
In the multidisciplinary field of Network Science, optimization of procedures for efficiently breaking complex networks is attracting much attention from a practical point of view. In this contribution, we present a module-based method to efficiently fragment complex networks. The procedure firstly identifies topological communities through which the network can be represented using a well established heuristic algorithm of community finding. Then only the nodes that participate of inter-community links are removed in descending order of their betweenness centrality. We illustrate the method by applying it to a variety of examples in the social, infrastructure, and biological fields. It is shown that the module-based approach always outperforms targeted attacks to vertices based on node degree or betweenness centrality rankings, with gains in efficiency strongly related to the modularity of the network. Remarkably, in the US power grid case, by deleting 3% of the nodes, the proposed method breaks the original network in fragments which are twenty times smaller in size than the fragments left by betweenness-based attack.
Highlights
Network theory and its applications pervade many scientific fields, like physics, sociology, engineering, epidemiology, biology, and many others
In order to quantify the effect of the attacks on the networks [38], we define G as an initial network of size N, and Gr as the network that results after the removal of a fraction ρ of vertices
The example networks we have chosen to study are of three types: infrastructural (US power grid, Euro road, Open flights and US airports) [39,40,41,42,43,44,45,46], biological (Yeast protein, C elegans and H pylori) [47,48,49] and social (Facebook, Google+ and Twitter) [50,51,52,53]
Summary
Network theory and its applications pervade many scientific fields, like physics, sociology, engineering, epidemiology, biology, and many others. The question we want to address in this contribution is: How to cause the same damage as the one resulting of a traditional centrality-based attack on a given network, but removing a smaller amount of nodes or edges?
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