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

• DOI: 10.1142/s0129183119500505
• Publication Date: 2019-05-17T05:38:05Z
• Authors: Jianxin Tang; Ruisheng Zhang; Yabing Yao; Zhili Zhao; Baoqiang Chai; Huan Li
• Keywords: Maximization

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