Discovering Influential Nodes for SIS Models in Social Networks

Authors:
Kazumi Saito;Masahiro Kimura;Hiroshi Motoda
Affiliations:
School of Administration and Informatics, University of Shizuoka, Shizuoka, Japan 422-8526;Department of Electronics and Informatics, Ryukoku University, Otsu, Shiga, Japan 520-2194;Institute of Scientific and Industrial Research, Osaka University, Osaka, Japan 567-0047
Venue:
DS '09 Proceedings of the 12th International Conference on Discovery Science
Year:
2009

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Abstract

We address the problem of efficiently discovering the influential nodes in a social network under the susceptible/infected/susceptible (SIS) model , a diffusion model where nodes are allowed to be activated multiple times. The computational complexity drastically increases because of this multiple activation property. We solve this problem by constructing a layered graph from the original social network with each layer added on top as the time proceeds, and applying the bond percolation with pruning and burnout strategies. We experimentally demonstrate that the proposed method gives much better solutions than the conventional methods that are solely based on the notion of centrality for social network analysis using two large-scale real-world networks (a blog network and a wikipedia network). We further show that the computational complexity of the proposed method is much smaller than the conventional naive probabilistic simulation method by a theoretical analysis and confirm this by experimentation. The properties of the influential nodes discovered are substantially different from those identified by the centrality-based heuristic methods.