An Overlapping Semantic Community Detection Algorithm Based on Local Semantic Cluster

Since the semantic social network (SSN) is a new kind of complex networks, the traditional community detection algorithms depending on the adjacency in social network are not efficient in the SSN. To solve this problem, an overlapping community structure detecting method on semantic social networks is proposed based on the local semantic cluster (LSC). Firstly, the algorithm utilizes the Gibbs sampling method to establish the quantization mapping by which the semantic information in nodes is changed into the semantic space, with the latent Dirichlet allocation (LDA) as the semantic model; Secondly, the algorithm establishes the similarity matrix of SSN, with the relative entropy of semantic coordinate as the measurement of similarity between nodes; Thirdly, according to the character of local small-world in social network, the algorithm proposes the S-fitness model which is the local community structure of SSN, and establishes the LSC method by the S-fitness model; Finally, the algorithm proposes the semantic model by which the community structure of SSN is measured, and the efficiency and feasibility of the algorithm and the semantic modularity are verified by experimental analysis.