Semantic community detection research based on topic probability models

Semantic social network (SSN) is a complex network consisting of textual node information and social relationships, and has more valuable applications than the traditional social network which focuses on social network analysis (SNA) with semantic information. As it contains the semantic analysis and social relationship analysis, there is some complexity for the modeling in mining the SNA community. In the SNA community mining aspect, the advantages and disadvantages of each method based on topic probability models are summarized to provide a theoretical basis for the further research. In the evaluation aspect of SNA community mining, relevant evaluation models are summarized. The tendency of each evaluation model toward topological and semantic relevance is compared by experimental analysis. Copyright © 2015 Acta Automatica Sinica. All rights reserved.