Self-supervised learning for community detection based on deep graph convolutional networks

To alleviate the excessive dependence of graph neural networks on prior knowledge in community discovery and improve recognition accuracy, a novel self-supervised learning model for community detection based on a deep graph convolutional network (GCN) is proposed. The model makes full use of the semantic features of a small number of nodes and obtains pseudo-labels of unknown nodes through a semantic alignment mechanism, and thus introduces a self-supervised module to alleviate the dependence on a large number of prior labels during the training of GCN. Furthermore, by stacking self-supervised modules, a deep graph self-supervised learning model is built to increase the accuracy of community detection by obtaining the global information of networks. Two strategies, identity mapping and initial residual, are employed to address the over-smoothing issues that the deep model introduces. According to experiments conducted on publicly available datasets, the suggested approach outperforms current models in terms of community recognition accuracy when a limited number of prior labels are used and the model depth is increased.