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A Community Detection Algorithm Based on Markov Random Walks Ants in Complex Network

  • Jian Ma (马健)Email author
  • Jianping Fan (樊建平)
  • Feng Liu (刘峰)
  • Honghui Li (李红辉)
Article
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Abstract

Complex networks display community structures. Nodes within groups are densely connected but among groups are sparsely connected. In this paper, an algorithm is presented for community detection named Markov Random Walks Ants (MRWA). The algorithm is inspired by Markov random walks model theory, and the probability of ants located in any node within a cluster will be greater than that located outside the cluster. Through the random walks, the network structure is revealed. The algorithm is a stochastic method which uses the information collected during the traverses of the ants in the network. The algorithm is validated on different datasets including computer-generated networks and real-world networks. The outcome shows the algorithm performs moderately quickly when providing an acceptable time complexity and its result appears good in practice.

Key words

complex network community detection Markov chain random walk 

CLC number

TP 391 

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References

  1. [1]
    NEWMAN M E J. Fast algorithm for detecting community structure in networks [J]. Physical Review E, 2004, 69(6): 066133.CrossRefGoogle Scholar
  2. [2]
    GIRVAN M, NEWMAN M E J. Community structure in social and biological networks [J]. Proceedings of the National Academy of Sciences of the United States of America, 2002, 99(12): 7821–7826.MathSciNetCrossRefzbMATHGoogle Scholar
  3. [3]
    RAGHAVAN U N, ALBERT R, KUMARA S. Near liner time algorithm to detect community structures in large-scale networks [J]. Physical Review E, 2007, 76(3): 036106.CrossRefGoogle Scholar
  4. [4]
    PONS P, LATAPY M. Computing communities in large networks using random walks [J]. Journal of Graph Algorithms and Applications, 2006, 10(2): 191–218.MathSciNetCrossRefzbMATHGoogle Scholar
  5. [5]
    ROSVALL M, BERGSTROM C T. Maps of random walks on complex networks reveal community structure [J]. Proceedings of the National Academy of Sciences of the United States of America, 2008, 105(4): 1118–1123.CrossRefGoogle Scholar
  6. [6]
    SU C, JIA X T, XIE X Z, et al. A new randomwalk based label propagation community detection algorithm [C]//IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology. Singapore: IEEE, 2015: 137–140.Google Scholar
  7. [7]
    KUNCHEVA Z, MONTANA G. Community detection in multiplex networks using locally adaptive random walks [C]//IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. Pairs, France: ACM, 2015: 1308–1315.Google Scholar
  8. [8]
    YANG B, CHEUNG W K, LIU J M. Community mining from signed social networks [J]. IEEE Transaction on Knowledge and Data Engineering, 2007, 19(10): 1333–1348.CrossRefGoogle Scholar
  9. [9]
    JIN D, YANG B, LIU J, et al. Ant colony optimization based on random walk for community detection in complex networks [J]. Journal of Software, 2012, 23(3): 451–464 (in Chinese).CrossRefzbMATHGoogle Scholar
  10. [10]
    ZHOU X, LIU Y H, ZHANG J D, et al. An ant colony based algorithm for overlapping community detection in complex networks [J]. Physica A: Statistical Mechanics and its Applications, 2015, 427: 289–301.CrossRefGoogle Scholar
  11. [11]
    NEWMAN M E J, GIRVAN M. Finding and evaluating community structure in networks [J]. Physical Review E, 2004, 69(2): 026113.CrossRefGoogle Scholar
  12. [12]
    ZACHARY W W. An information flow model for conflict and fission in small groups [J]. Journal of Anthropological Research, 1977, 33(4): 452–473.CrossRefGoogle Scholar
  13. [13]
    LUSSEAU D. The emergent properties of a dolphin social network [J]. Proceedings of the Royal Society of London. Series B: Biological Sciences, 2003, 270(Sup2): 186–188.Google Scholar
  14. [14]
    NEWMAN M E J. Modularity and community structure in networks [J]. Proceedings of the National Academy of Sciences of the United States of America, 2006, 103(23): 8577–8582.CrossRefGoogle Scholar

Copyright information

© Shanghai Jiaotong University and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Jian Ma (马健)
    • 1
    Email author
  • Jianping Fan (樊建平)
    • 1
  • Feng Liu (刘峰)
    • 1
  • Honghui Li (李红辉)
    • 1
  1. 1.School of Computer and Information TechnologyBeijing Jiaotong UniversityBeijingChina

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