GCN-based weakly-supervised community detection with updated structure centres selection

Abstract

Community detection is a classic problem in network learning. Semi-supervised network learning requires a certain amount of known samples, while sample annotation is time-consuming and laborious. In particular, when the number of known samples is only very small, the learning ability of existing semi-supervised network learning models decreases sharply. In view of this, a weakly-supervised community detection method based on graph convolutional neural network (WC-GCN). Firstly, it introduces a genetic evolution strategy to select and update the structure centres, which enables the updating structure centre process to not get stuck in the local optima, and get the structural centres that are closer to the global best, solving the problem of centre dependence. Secondly, the structural centrality index Cstruct is proposed to measure the representativeness of a subgraph, learning more accurate network structure centres. Thirdly, a self-training method to expand the pseudo-labelled nodes for GCN training to further improve the model effect. The proposed method is evaluated on various real-world networks and shows that it outperforms the state-of-the-art community detection algorithms.