Personalized Web Page Ranking Based Graph Convolutional Network for Community Detection in Attribute Networks

Identifying key information from complex networks is of great practical significance for discovering the community structure. How to effectively use the information of node connections in the network and the attribute information in the attribute network is a major challenge in the current community detection problem of attribute networks. A graph convolutional network based on personalized web page ranking algorithm is proposed for community detection in attribute networks in this paper. First, the proposed algorithm uses the strong characteristics of graph convolution algorithm for integrating node topology and attribute information, and combines with personalized web page ranking algorithm to decouple the prediction process and propagation process in the model. In addition, the improved density peak detection method is used to sample the local structure center as a training set for training the algorithm model. Finally, k-means method is used to cluster the node vector representation results and get the community division. The experimental results on 7 datasets with 13 comparison algorithms show that the proposed algorithm has obvious improvement for attribute community detection.