An adaptive discrete particle swarm optimization for influence maximization based on network community structure

Abstract

As an important research field of social network analysis, influence maximization problem is targeted at selecting a small group of influential nodes such that the spread of influence triggered by the seed nodes will be maximum under a given propagation model. It is yet filled with challenging research topics to develop effective and efficient algorithms for the problem especially in large-scale social networks. In this paper, an adaptive discrete particle swarm optimization (ADPSO) is proposed based on network topology for influence maximization in community networks. According to the framework of ADPSO, community structures are detected by label propagation algorithm in the first stage, then dynamic encoding mechanism for particle individuals and discrete evolutionary rules for the swarm are conceived based on network community structure for the meta-heuristic optimization algorithm to identify the allocated number of influential nodes within different communities. To expand the seed nodes reasonably, a local influence preferential strategy is presented to allocate the number of candidate nodes to each community according to its marginal gain. The experimental results on six social networks demonstrate that the proposed ADPSO can achieve comparable influence spread to CELF in an efficient way.