Boosting A Non-Negative Matrix Factorization-Based Community Detector via Graph Convolution Regularization

Community detection sheds light on various graph mining tasks such as social recommendation, which is becoming a long-standing issue in the realm of complex network analysis. A non-negative matrix factorization (NMF) is frequently used to tackle this task. However, it builds based on the principle of linear representation and has difficulty in capturing non-linear features from irregularly non-Euclidean data. To address this issue, this study boosts an NMF-based community detector by combining with a graph convolution module, and a novel Graph Convolution and Graph-Laplacian bi-regularized, Symmetric non-negative matrix factorization (GCGS) model is proposed relying on two main ideas: a) taking a graph convolution network (GCN) as a non-linear constraint module on the feature matrix to ensure its non-linearity; and b) adopting graph regularization to preserve the local geometric features of the network topology. A non-negative and multiplicative update (NMU) algorithm is then derived to solve the unified objective function. Extensive experimental results on six real networks indicate that GCGS achieves higher precision in community detection than its peers.