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A Neighborhood-Impact Based Community Detection Algorithm via Discrete PSO

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  • Dongqing Zhou
  • Xing Wang

Abstract

The paper addresses particle swarm optimization (PSO) into community detection problem, and an algorithm based on new label strategy is proposed. In contrast with other label propagation strategies, the main contribution of this paper is to design the definition of the impact of node and take it into use. Special initialization and update approaches based on it are designed in order to make full use of it. Experiments on synthetic and real-life networks show the effectiveness of proposed strategy. Furthermore, this strategy is extended to signed networks, and the corresponding objective function which is called modularity density is modified to be used in signed networks. Experiments on real-life networks also demonstrate that it is an efficacious way to solve community detection problem.

Suggested Citation

  • Dongqing Zhou & Xing Wang, 2016. "A Neighborhood-Impact Based Community Detection Algorithm via Discrete PSO," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-15, January.
    • Handle: RePEc:hin:jnlmpe:3790590
    DOI: 10.1155/2016/3790590
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    Cited by:

    1. He, Zhipeng & Zhang, Shuguang & Hu, Jun & Dai, Fei, 2024. "An adaptive time series segmentation algorithm based on visibility graph and particle swarm optimization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 636(C).

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