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Community Detection in Networks with Less Significant Community Structure

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Book cover Advanced Data Mining and Applications (ADMA 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10086))

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Abstract

Label propagation is a low complexity approach to community detection in complex networks. Research has extended the basic label propagation algorithm (LPA) in multiple directions including maximizing the modularity, a well-known quality function to evaluate the goodness of a community division, of the detected communities. Current state-of-the-art modularity-specialized label propagation algorithm (LPAm+) maximizes modularity using a two-stage iterative procedure: the first stage is to assign labels to nodes using label propagation, the second stage merges smaller communities to further improve modularity. LPAm+ has been shown able to achieve excellent performance on networks with significant community structure where the network modularity is above a certain threshold. However, we show in this paper that for networks with less significant community structure, LPAm+ tends to get trapped in local optimal solutions that are far from optimal. The main reason comes from the fact that the first stage of LPAm+ often misplaces node labels and severely hinders the merging operation in the second stage. We overcome the drawback of LPAm+ by correcting the node labels after the first stage. We apply a label propagation procedure inspired by the meta-heuristic Record-to-Record Travel algorithm that reassigns node labels to improve modularity before merging communities. Experimental results show that the proposed algorithm, named meta-LPAm+, outperforms LPAm+ in terms of modularity on networks with less significant community structure while retaining almost the same performance on networks with significant community structure.

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Acknowledgments

The authors would like to thank the maintainers and contributors of the igraph packages used in this research.

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Authors and Affiliations

  1. School of Computer Science, The University of Adelaide, Adelaide, 5005, Australia

    Ba-Dung Le, Hung Nguyen & Hong Shen

Authors
  1. Ba-Dung Le
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  2. Hung Nguyen
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  3. Hong Shen
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Corresponding author

Correspondence to Ba-Dung Le .

Editor information

Editors and Affiliations

  1. University of Technology , Sydney, New South Wales, Australia

    Jinyan Li

  2. University of Queensland , Brisbane, Australia

    Xue Li

  3. Beijing Institute of Technology , Beijing, China

    Shuliang Wang

  4. University of Western Australia , Crawley, West Australia, Australia

    Jianxin Li

  5. University of Adelaide , Adelaide, South Australia, Australia

    Quan Z. Sheng

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Le, BD., Nguyen, H., Shen, H. (2016). Community Detection in Networks with Less Significant Community Structure. In: Li, J., Li, X., Wang, S., Li, J., Sheng, Q. (eds) Advanced Data Mining and Applications. ADMA 2016. Lecture Notes in Computer Science(), vol 10086. Springer, Cham. https://doi.org/10.1007/978-3-319-49586-6_5

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  • DOI: https://doi.org/10.1007/978-3-319-49586-6_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-49585-9

  • Online ISBN: 978-3-319-49586-6

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