Graph Convolutional Networks Meet Markov Random Fields: Semi-Supervised Community Detection in Attribute Networks - Jin, Di; Liu, Ziyang; Li, Weihao; He, Dongxiao; Zhang, Weixiong (Proceedings of the AAAI Conference on Artificial Intelligence, 2019)
Community detection is a fundamental problem in network science with various applications. The problem has attracted much attention and many approaches have been proposed. Among the existing approaches are the latest methods based on Graph Convolutional Networks (GCN) and on statistical modeling of Markov Random Fields (MRF). Here, we propose to integrate the techniques of GCN and MRF to solve the problem of semi-supervised community detection in attributed networks with semantic information. Our new method takes advantage of salient features of GNN and MRF and exploits both network topology and node semantic information in a complete end-to-end deep network architecture. Our extensive experiments demonstrate the superior performance of the new method over state-of-the-art methods and its scalability on several large benchmark problems.