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

Jianxin Tang, Ruisheng Zhang (), Yabing Yao, Zhili Zhao, Baoqiang Chai and Huan Li
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Jianxin Tang: School of Computer and Communication, Lanzhou University of Technology, Lanzhou, Gansu 730050, P. R. China†School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu 730000, P. R. China
Ruisheng Zhang: #x2020;School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu 730000, P. R. China
Yabing Yao: School of Computer and Communication, Lanzhou University of Technology, Lanzhou, Gansu 730050, P. R. China
Zhili Zhao: #x2020;School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu 730000, P. R. China
Baoqiang Chai: #x2020;School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu 730000, P. R. China
Huan Li: #x2020;School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu 730000, P. R. China

International Journal of Modern Physics C (IJMPC), 2019, vol. 30, issue 06, 1-21

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.

Keywords: Social networks; viral marketing; influence maximization; community detection; adaptive discrete particle swarm optimization (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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DOI: 10.1142/S0129183119500505

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