Self-supervised graph autoencoder with redundancy reduction for community detection

Community detection is a significant research topic in network science, which has been revisited with graph neural networks. As a powerful graph representation learning model, graph autoencoder (GAE) is commonly used for unsupervised community detection. However, most GAE-based methods ignore multi-scale features of encoding layers, which inherently provide useful information for community detection. Moreover, these methods fail to simultaneously improve the representation learning process and clustering performance through a unified objective function. To address these issues, we propose a self-supervised graph autoencoder model with redundancy reduction for community detection. Firstly, we introduce a multi-scale module based on GAE to obtain discriminative node representations from different encoding layers. In particular, a redundancy reduction strategy is employed to eliminate redundancy information in the latent embedding space. Then, a node clustering module is used to obtain initial community labels. To fully utilize the multi-scale features to further refine clustering performance, a self-supervised module is designed to utilize current clustering labels to supervise the node representation learning process, thus constructing an end-to-end model for community detection. Finally, we validate the effectiveness of the proposed method on real-world networks. Experimental results demonstrate that our method outperforms several state-of-the-art methods in community detection.