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Modularity Maximization for Community Detection Using Genetic Algorithm

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Neural Information Processing (ICONIP 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11302))

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Abstract

Modularity function is a widely-used criterion to evaluate the strength of community structure in community detection. In this paper, we propose a modularity maximization method for detecting communities, based on genetic algorithm and random walk model, and propose a new community structure encoding method for networks. First, the random walk model was applied to calculate the similarity between nodes, resulting in a weighted matrix as derived from the original adjacency matrix. According to the nearest neighbor-based similarity representation provisional, a weighted network connection structure was then coded into a chromosome. The genetic algorithm modified the structure of a predefined number of chromosomes and computed the corresponding modularity, ultimately yielding the maximum value of modularity as it corresponds to community structure and number of communities. We tested this method on a series of real social networks. Compared with several state-of-the-art methods, the novel method obtained both greater modularity value. Thus, results by the proposed method are more practical, since this method does not require specified number of communities at the outset of community partition. Here, the optimal number of communities and community structures are automatically determined.

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Acknowledgments

This study was supported by the National Natural Science Foundation of China (Project No. 61375122 and Project No. 61572239). Scientific Research Foundation for Advanced Talents of Jiangsu University (Project No. 14JDG040). Postgraduate Research & Practice Innovation Program of Jiangsu Province (Project No. SJCX18_0741).

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Authors and Affiliations

  1. School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, 212003, China

    Hu Lu & Qi Yao

Authors
  1. Hu Lu
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  2. Qi Yao
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Correspondence to Hu Lu .

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Editors and Affiliations

  1. The Chinese Academy of Sciences, Beijing, China

    Long Cheng

  2. City University of Hong Kong, Kowloon, Hong Kong

    Andrew Chi Sing Leung

  3. Kobe University, Kobe, Japan

    Seiichi Ozawa

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Lu, H., Yao, Q. (2018). Modularity Maximization for Community Detection Using Genetic Algorithm. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11302. Springer, Cham. https://doi.org/10.1007/978-3-030-04179-3_41

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  • DOI: https://doi.org/10.1007/978-3-030-04179-3_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04178-6

  • Online ISBN: 978-3-030-04179-3

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