Finding critical nodes for inhibiting diffusion of complex contagions in social networks

Authors:
Chris J. Kuhlman;V. S. Anil Kumar;Madhav V. Marathe;S. S. Ravi;Daniel J. Rosenkrantz
Affiliations:
Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA;Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA;Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA;Computer Science Department, University at Albany - SUNY, Albany, NY;Computer Science Department, University at Albany - SUNY, Albany, NY
Venue:
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Year:
2010

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Abstract

We study the problem of inhibiting diffusion of complex contagions such as rumors, undesirable fads and mob behavior in social networks by removing a small number of nodes (called critical nodes) from the network. We show that, in general, for any ρ ≥ 1, even obtaining a ρ-approximate solution to these problems is NP-hard. We develop efficient heuristics for these problems and carry out an empirical study of their performance on three well known social networks, namely epinions, wikipedia and slashdot. Our results show that the heuristics perform well on the three social networks.