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)


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 

Document code


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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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