Attribute community detection based on latent representation learning and graph regularized non-negative matrix factorization

Abstract

In recent years, complex networks have become increasingly difficult to detect effectively with the rapid development of networks. On the one hand, the scale of the network has increased sharply, and on the other hand, the nodes in the network contain rich content. The existing algorithms do not take the different richness of attribute information of nodes in the attribute network into account. And the topology information of the node is inconsistent with the attribute information. Thus, this paper proposes an attribute community detection algorithm based on latent representation learning and graph-regularized non-negative matrix factorization (LRL-GNMF). First, the topological information and attribute information in the attribute network is decomposed based on non-negative matrix factorization respectively. Thereby, the member distribution matrix and the attribute distribution matrix are obtained. Secondly, an affinity matrix is constructed for the attribute matrix, and the latent representation of the attribute information is obtained using the latent representation learning method. In addition, a transition matrix is constructed according to the Markov transition probability. The node membership distribution matrix and the attribute distribution matrix are linked. Finally, since the attribute information of nodes in the network is different, a topology-dominated model and an attribute-dominated model are respectively constructed to solve this problem. At the same time, a graph regularization term is introduced to guide the model to obtain more accurate community detection. This paper conducts experimental analysis on 8 real networks. The experimental results show that the proposed algorithm in this paper outperforms the other 11 compared algorithms in the evaluation indicators of community detection accuracy and standard mutual information.