Weakly-supervised learning for community detection based on graph convolution in attributed networks

摘要

Community detection in complex networks has been revisited with graph deep learning recently and has attracted great attention. It is often challenging to uncover underlying communities on attributed networks because of the complexity and diversity of graph-structured data. A recent prominent graph deep learning model is graph convolutional network (GCN), which effectively integrates network topology and attribute information in graph representation learning. However, most GCN-based community detection methods are semi-supervised and require a considerable amount of labeled data for training. Here, we propose a weakly-supervised learning method based on GCN for community detection in attributed networks. Our new method integrates the techniques of GCN and label propagation and the latter constructs a balanced label set to uncover underlying community structures with topology and attribute information. The experiments on various real-world networks give a comparison view to evaluate the proposed method. The experimental result demonstrates the proposed method performs more efficiently with a comparative performance over current state-of-the-art community detection algorithms.