关键词: 社区发现, 用户生成内容, 用户链接关系, 社会网络

Abstract: Community discovery has been an attractive field in social networks research. However, in current community discovery algorithms, more attention is attracted to the link relationships between users and little attention is paid on big data of user generated contents (UGC). User generated content is a special feature of Web2.0, and also is an important reason to attract users, which plays an important role to form communities. This paper presents a new algorithm to solve the community discovery problem, which comprehensively utilizes the link relationships between users and user generated contents. This algorithm uses latent Dirichlet allocation (LDA) algorithm to analyze text information generated by users, and uses spectral analysis method to analyze the link relationships between users, and combines them to discovery communities. By analyzing real world data — science network site data, the proposed algorithm is proved to effectively utilize the user generated contents and link relationships between users, and the results are more objective and accurate.

Key words: community discovery, user generated contents, user link relationships, social networks