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An Enhanced Particle Swarm Optimization Based on Physarum Model for Community Detection

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Advances in Swarm Intelligence (ICSI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10386))

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Abstract

Community detection, an effective tool to analyze and understand network data, has been paid more and more attention in recent years. One of the most popular methods of detecting community structure is to find the division with the maximal modularity. However, the modularity maximization is an NP-complete problem. In the field of swarm intelligence algorithm, particle swarm optimization (PSO) has been widely used to solve such NP-complete problem. Nevertheless, premature convergence and lower accuracy limit its performance in community detection. In order to overcome these shortcomings, this paper proposes a novel PSO called P-PSO for community detection through combining the computational ability of Physarum, a kind of slime. The proposed algorithm improves the efficiency of PSO by recognizing inter-community edges based on Physarum-inspired network model (PNM). Experiments in eight networks show that the proposed algorithm is effective and promising for community detection, compared with other algorithms.

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Acknowledgments

Zhengpeng Chen and Fanzhen Liu contributed equally to this work and should be considered as co-first authors. This work is supported by the National Natural Science Foundation of China (Nos. 61402379, 61403315), Fundamental Research Funds for the Central Universities (No. XDJK2016A008, XDJK2016B029, XDJK2016E074), CQ CSTC (cstc2015gjhz40002).

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Authors and Affiliations

  1. School of Computer and Information Science, Southwest University, Chongqing, 400715, China

    Zhengpeng Chen, Fanzhen Liu, Chao Gao, Xianghua Li & Zili Zhang

  2. Potsdam Institute for Climate Impact Research (PIK), 14473, Potsdam, Germany

    Chao Gao

  3. Institute of Physics, Humboldt-University zu Berlin, Berlin, 12489, Germany

    Chao Gao

  4. School of Information Technology, Deakin University, Geelong, VIC, 3220, Australia

    Zili Zhang

Authors
  1. Zhengpeng Chen
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  2. Fanzhen Liu
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  3. Chao Gao
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  4. Xianghua Li
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  5. Zili Zhang
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Corresponding author

Correspondence to Chao Gao .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Kyushu University, Fukuoka, Japan

    Hideyuki Takagi

  3. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

  4. Shenzhen University, Shenzhen, China

    Ben Niu

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Chen, Z., Liu, F., Gao, C., Li, X., Zhang, Z. (2017). An Enhanced Particle Swarm Optimization Based on Physarum Model for Community Detection. In: Tan, Y., Takagi, H., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2017. Lecture Notes in Computer Science(), vol 10386. Springer, Cham. https://doi.org/10.1007/978-3-319-61833-3_11

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  • DOI: https://doi.org/10.1007/978-3-319-61833-3_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61832-6

  • Online ISBN: 978-3-319-61833-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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