Node Clustering Based on Multi-channel Graph Convolutional Network

To solve the problem that most methods based on graph convolutional network (GCN) only use topological graph and ignore the structural information in the feature space in deep clustering, a node clustering method was proposed by introducing feature graph to make full use of the structural information in the feature space. First, an auto-encoder (AE) was used to learn the potential representation of node features, and the node embeddings were obtained at the three levels of feature graph, topology graph and node attribute at the same time. Then, a fusion mechanism was used to fuse the learned node embeddings. Finally, the network was trained by self-supervision to implement node clustering. A large number of expiments on six benchmark datasets show that the proposed method significantly improves the clustering accuracy.