Unsupervised Community Detection Algorithm Based on Graph Convolution Network and Social Media

Abstract

In view of the difficulty and low efficiency of most existing algorithms in detecting large-scale community networks, an unsupervised community detection algorithm based on graph convolution networks and social media is proposed. First, some positive and negative sample nodes are labeled according to the node similarity to complete the graph segmentation. Then, the improved graph convolution network model is used for training to obtain the local community where the given starting node is located. Finally, the local community is optimized by setting the threshold of membership degree, so as to further screen the nodes outside the community and obtain accurate community detection results. The experimental analysis of the proposed algorithm based on Flixster, Douban, and Yelp datasets shows that when the number of community divisions is 12, the modularity values on the three datasets are 0.59, 0.62, and 0.69, respectively, and the standard deviations of F1 are 0.044, 0.048, and 0.040, respectively. Overall, the proposed unsupervised community detection algorithm has better robustness.