Method of K-order Graph Convolution Attribute Network Community Detection

Mining community structure in attribute network is helpful to further analysis of network nodes, which has important practical significance. Graph convolution neural network can effectively embed the structural information of attribute network and obtain the feature representation of nodes. Based on this, the community structure with good performance can be obtained. However, most of the existing graph convolution methods use fixed low-order graph convolution, only consider the first-order or second-order neighbors of each node, don’t make full use of the node relationship and ignore the diversity of network structure. In addition, the sparsity of the original network structure can’t be overcome, which will reduce the performance of community detection. In order to solve the above problems, this paper proposes a K-order graph convolution community detection method (KGCN) which combines attribute information and structure information. This method can effectively overcome the sparsity of the original network and use the high-order structure of nodes for community detection. Firstly, the original network is reconstructed according to the attribute information of nodes to alleviate the sparsity of the original network. Secondly, considering the high-order structure correlation, the K-order graph convolution encoder is used to encode the nodes to obtain the feature representation of the nodes. Finally, community detection is carried out using spectral clustering algorithm. Experimental results show that the KGCN method achieves better community detection results than the existing algorithms on four real datasets.