Overlapping Community Detection Algorithm Based on Improved MCMC Method

doi:10.3969/j.issn.1671-1122.2017.09.032

Abstract

Abstract:

This paper analyzes the characteristics and mechanism of the overlapping community detection algorithm based on Community-Affiliation Graph Model AGM. The aim of this paper is to improve the partial optimization problem .In the original MCMC sampling method, the simulated annealing (ST) strategy and the supplementary search process were introduced to realize the fast updating of the parameters to be obtained and to approximate the global optimal solution. Experiments in four networks show that the results of the improved algorithm are improved compared with the original algorithm, and the experimental results in the DBLP scientific co-network with higher average clustering coefficient are improved by 14%. The improved algorithm can improve the efficiency of sampling and improve the accuracy and reliability of community detection.

Key words: complex network, overlapping community, MCMC method, maximal likelihood

CLC Number:

TP393

Shunshun FU, Yijun GU, Dahan ZHANG, Fanpeng MENG. Overlapping Community Detection Algorithm Based on Improved MCMC Method[J]. Netinfo Security, 2017, 17(9): 138-142.

Figures/Tables 8

References 14

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