Journal of Shanghai Jiao Tong University (Science) ›› 2019, Vol. 24 ›› Issue (1): 71-77.doi: 10.1007/s12204-019-2041-2

MA Jian *(马健), FAN Jianping (樊建平), LIU Feng (刘峰), LI Honghui (李红辉)

- (School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China)

MA Jian *(马健), FAN Jianping (樊建平), LIU Feng (刘峰), LI Honghui (李红辉)

- (School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China)

**摘要： **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.

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