Abstract: Community discovery in attributed networks is an important research content in network data analysis.To improve the accuracy of community discovery,most existing algorithms perform low-dimensional representation of attributed networks by fusing topological and attributed information,and then perform community discovery based on low-dimensional features.Such algorithms,however,are typically based on deep learning models for representation learning,which lack interpretability.Therefore,in order to improve the accuracy and interpretability of community discovery results,this paper proposes a community discovery algorithm for attributed networks based on bipartite graph representation.Firstly,the topological and attributed information of the attributed networks are used to calculate the probability of each node serving as a representative point in the network,and a certain proportion of nodes are chosen as representative points.Secondly,based on the topological structure and node attributes,the distances of each node to the representative points are calculated to construct a bipartite graph.Finally,based on the bipartite graph,the result is obtained by using the spectral clustering algorithm for community discovery.Experiments are carried out on artificial and real attributed networks to compare and analyze the proposed algorithm and the existing algorithms.In terms of evaluation indices such as normalized mutual information and adjusted rand index,experimental results show that the proposed algorithm outperforms the existing algorithms.

Key words: Attributed networks, Community discovery, Bipartite graph, Fusion