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

被引：0

作者：

马健 ^{[1
]}

樊建平 ^{[1
]}

刘峰 ^{[1
]}

李红辉 ^{[1
]}

机构：

[1] School of Computer and Information Technology, Beijing Jiaotong University

来源：

JournalofShanghaiJiaotongUniversity(Science) | 2019年 / 24卷 / 01期

关键词：

complex network; community detection; Markov chain; random walk;

D O I：

暂无

中图分类号：

TP18 [人工智能理论]; O157.5 [图论];

学科分类号：

070104 ; 081104 ; 0812 ; 0835 ; 1405 ;

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.

引用

页码：71 / 77

页数：7

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