Research on Community Discovery Algorithm Based on Network Structure and Multi-dimensional User Information

Recently, most of the community discovery algorithms are based on the structural information of undirected networks, and the social characteristics of users are less considered. Based on the academic social network, we propose a label propagation algorithm that integrates the network structure and multidimensional user information (LPA-NU). Through the fusion of multidimensional social networks, the algorithm firstly uses the LDA model to mine the similarity of user research directions to derive the hidden social edges between users. Secondly, it constructs a comprehensive directed weighted network, and then classifies the community according to the initial sub-group information. In order to evaluate the quality of community discovery, this paper proposes the definition of overlapping modules of directed networks. We conduct relevant experiments on real social network datasets (SCHOLAT). Experiments show that the LPA-NU algorithm can better divide the structure of the community, and the quality of community division is higher.