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Finding communities in directed networks by PageRank random walk induced network embedding

Author

Listed:
  • Lai, Darong
  • Lu, Hongtao
  • Nardini, Christine

Abstract

Community structure has been found to exist ubiquitously in many different kinds of real world complex networks. Most of the previous literature ignores edge directions and applies methods designed for community finding in undirected networks to find communities. Here, we address the problem of finding communities in directed networks. Our proposed method uses PageRank random walk induced network embedding to transform a directed network into an undirected one, where the information on edge directions is effectively incorporated into the edge weights. Starting from this new undirected weighted network, previously developed methods for undirected network community finding can be used without any modification. Moreover, our method improves on recent work in terms of community definition and meaning. We provide two simulated examples, a real social network and different sets of power law benchmark networks, to illustrate how our method can correctly detect communities in directed networks.

Suggested Citation

  • Lai, Darong & Lu, Hongtao & Nardini, Christine, 2010. "Finding communities in directed networks by PageRank random walk induced network embedding," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(12), pages 2443-2454.
    • Handle: RePEc:eee:phsmap:v:389:y:2010:i:12:p:2443-2454
    DOI: 10.1016/j.physa.2010.02.014
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    Citations

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    Cited by:

    1. Li, Yafang & Jia, Caiyan & Yu, Jian, 2015. "A parameter-free community detection method based on centrality and dispersion of nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 321-334.
    2. Zhu, Canshi & Wang, Xiaoyang & Zhu, Lin, 2017. "A novel method of evaluating key nodes in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 96(C), pages 43-50.

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