A Semantic Overlapping Community Detecting Algorithm in Social Networks Based on Random Walk

Since the semantic social networks (SSN) is a new kind of complex networks, the community detection is a new investigation relevant to the traditional community detection research. To solve this problem, an overlapping community structure detecting method in semantic social network is proposed based on the random walk strategy. The algorithm establishes the semantic space using latent Dirichlet allocation (LDA) method. Firstly, the quantization mapping is completed by which semantic information in nodes can be changed into the semantic space. Secondly, the semantic influence model and weighed adjacent matrix of SSN are established, with the entropy of nodes in SSN as the semantic information proportion, the distribution ratio of nodes as the weight of adjacent. Thirdly, an improved random walk strategy of community structure detecting in overlapping-SSN is proposed, with the distribution ratio of nodes as parameter, and a semantic modularity model is proposed by which the community structure of SSN can be measured. Finally, the efficiency and feasibility of the proposed algorithm and the semantic modularity are verified by experimental analysis.