Joint Weighted Nonnegative Matrix Factorization for Mining Attributed Graphs

Graph clustering has been extensively studied in the past decades, which can serve many real world applications, such as community detection, big network management and protein network analysis. However, the previous studies focus mainly on clustering with graph topology information. Recently, as the advance of social networks and Web 2.0, many graph datasets produced contain both the topology and node attribute information, which are known as attributed graphs. How to effectively utilize the two types of information for clustering thus becomes a hot research topic. In this paper, we propose a new attributed graph clustering method, JWNMF, which integrates topology structure and node attributes by a new collective nonnegative matrix factorization method. On the one hand, JWNMF employs a factorization for topology structure. On the other hand, it designs a weighted factorization for nodes' attributes, where the weights are automatically determined to discriminate informative and uninformative attributes for clustering. Experimental results on seven real-world datasets show that our method significantly outperforms state-of-the-art attributed graph clustering methods.