Community Detection Algorithm in Multi-Relational Networks

Authors

Jingping Yu, 1. School of Engineering Research Institute, Jiangxi University of Science and Technology, Ganzhou 341000, China;;
Zheng Jie, 2. School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China;
Guixiang Zhu, 2. School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China;

Abstract

Abstract: In view of the traditional community detection algorithms being mainly applied to single relational networks, ignoring the interaction of relationship in the multi-relational networks, being unable to distinguish the importance of each relation for community detection, a novel algorithm called InteractRank was proposed. Based on the node and the relation of ranking model, the algorithm could transform multi-relational network into single relational network. Combined the PageRank algorithm and the random walk model, the algorithm considered the connection within groups and between groups in multi-relational networks. After transforming into single relational networks, spectral clustering algorithm was adopted to detect community. Through the simulation experiments on the standard UCI dataset, InteractRank indicates to be effective to community detection in multi-relational networks.

Recommended Citation

Yu, Jingping; Jie, Zheng; and Zhu, Guixiang (2020) "Community Detection Algorithm in Multi-Relational Networks," Journal of System Simulation: Vol. 27: Iss. 1, Article 20.
Available at: https://dc-china-simulation.researchcommons.org/journal/vol27/iss1/20

First Page

147

Revised Date

2014-03-03

DOI Link

https://doi.org/

Last Page

154

CLC

TP393

Recommended Citation

Yu Jingping, Zheng Jie, Zhu Guixiang. Community Detection Algorithm in Multi-Relational Networks[J]. Journal of System Simulation, 2015, 27(1): 147-154.