Community detection for multi-layer social network based on local random walk

Abstract

With the fast development of information network, the scale of social network has become very significant, and it has become more difficult to obtain the information of entire network. In addition, because current mining method for complicated network community utilizes the information of node link or property, which cannot effectively detect the community with dense member links and highly similar properties. As a result, most current algorithms are impractical for online social network with large scale, and we propose a community detection algorithm for multi-layer social network based on local random walk (MRLCD); this algorithm determines the core node based on the repeatability of multi-layer nodes. It expands from a core node, has local random walk in multi-layer network, identifies and controls the random walk scope of node based on the intra-layer and interlayer trust. During the walk process, the clustering coefficient of nodes to be combined is comprehensively compared to further complete a local community search, and the optimal local community search is obtained through multiple iterations. Finally, the multi-layer modularity is used as the indicator for measurement and evaluation of algorithm performance, and its performance is compared with other network clustering algorithms such as GL, LART and PMM through four actual multi-layer network datasets. The MRLCD algorithm can autonomously explore the local community structure of given node, and effectively improve the stability and accuracy for local community detection in multi-layer social network.