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Online Community Detection for Large Complex Networks

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  • Gang Pan
  • Wangsheng Zhang
  • Zhaohui Wu
  • Shijian Li

Abstract

Complex networks describe a wide range of systems in nature and society. To understand complex networks, it is crucial to investigate their community structure. In this paper, we develop an online community detection algorithm with linear time complexity for large complex networks. Our algorithm processes a network edge by edge in the order that the network is fed to the algorithm. If a new edge is added, it just updates the existing community structure in constant time, and does not need to re-compute the whole network. Therefore, it can efficiently process large networks in real time. Our algorithm optimizes expected modularity instead of modularity at each step to avoid poor performance. The experiments are carried out using 11 public data sets, and are measured by two criteria, modularity and NMI (Normalized Mutual Information). The results show that our algorithm's running time is less than the commonly used Louvain algorithm while it gives competitive performance.

Suggested Citation

  • Gang Pan & Wangsheng Zhang & Zhaohui Wu & Shijian Li, 2014. "Online Community Detection for Large Complex Networks," PLOS ONE, Public Library of Science, vol. 9(7), pages 1-12, July.
    • Handle: RePEc:plo:pone00:0102799
    DOI: 10.1371/journal.pone.0102799
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    References listed on IDEAS

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    1. Carlo Ratti & Stanislav Sobolevsky & Francesco Calabrese & Clio Andris & Jonathan Reades & Mauro Martino & Rob Claxton & Steven H Strogatz, 2010. "Redrawing the Map of Great Britain from a Network of Human Interactions," PLOS ONE, Public Library of Science, vol. 5(12), pages 1-6, December.
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    Cited by:

    1. Ghosh, Sumita, 2021. "Urban agriculture potential of home gardens in residential land uses: A case study of regional City of Dubbo, Australia," Land Use Policy, Elsevier, vol. 109(C).

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