Discovering Influential Nodes for SIS Models in Social Networks

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 ex...

• Type: Conference
• Year: 2009
• Where: DIS
• Authors: Kazumi Saito, Masahiro Kimura, Hiroshi Motoda
• Keywords: Computational Complexity, DIS 2009, Influential Nodes, Social Network, Theoretical Computer Science