A robust multi-view clustering method for community detection combining link and content information

Community detection is an important problem of complex networks analysis and various methods have been proposed to solve it. However, most of the existing methods only use the link information. As a result, the quality of their detected communities is often poor due to the sparse and noisy data existing in link information. Actually, content information of complex networks can also help to improve the quality of community detection. In this paper, we propose a method based on Multi-View Clustering via Robust Nonnegative Matrix Factorization (MVCRNMF). This method can provide a unified framework to combine link and content information for community detection. Its key idea is to build a multi-view robust NMF model with the co-regularized constraint on community indicator matrices of link view and content view. This can make link and content information complement each other during the factorization process of NMF. We devise iterative update rules as the optimization solution to the community detection model and also give the rigorous convergence proof. It is worth noting that MVCRNMF can learn the contribution weights from link and content information adaptively and this helps to save a lot of time on tuning the weight parameters. We conduct comparative experiments on four real-world complex networks. The results demonstrate that MVCRNMF performs better than state-of-the-art methods. Additionally, results of the case study on a co-authorship network also show that MVCRNMF can obtain higher quality communities.