Masked Dual Graph Autoencoder for Attributed Graph Community Detection

Community detection intends to cluster graph nodes with relevant information, and community detection for attributed graphs is of great practical importance. However, the existing work is still insufficient in terms of the representation of network topology and attribute information. In addition, the impact of noise and missing attributes on attributed graph representation learning has been rarely considered. To address the above issues, this paper proposes an innovative masked dual graph autoencoder (MDGAE) method for attributed graph community detection. The method consists of a graph attention autoencoder module, a masked graph autoencoder module, a graph representation fusion module, and a self-optimizing clustering module. Firstly, MDGAE fully explores graph topological and attribute information through the graph attention autoencoder and the masked graph autoencoder, respectively, and employs a masking strategy to solve the problem of noise and missing attributes efficiently; then, the low-dimensional representations obtained from the two autoencoders are weighted and fused for clustering; finally, the clustering results are iteratively optimized by joining with the self-optimizing clustering module. The experimental results on three benchmark datasets demonstrate the advantages of the MDGAE method over the existing algorithms.