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Graph clustering algorithms with autoencoder structures have recently gainedpopularity due to their efficient performance and low training cost. However,for existing graph autoencoder clustering algorithms based on GCN or GAT, notonly do they lack good generalization ability, but also the number of clustersclustered by such autoencoder models is difficult to determine automatically.To solve this problem, we propose a new framework called Graph Clustering withMasked Autoencoders (GCMA). It employs our designed fusion autoencoder based onthe graph masking method for the fusion coding of graph. It introduces ourimproved density-based clustering algorithm as a second decoder while decodingwith multi-target reconstruction. By decoding the mask embedding, our model cancapture more generalized and comprehensive knowledge. The number of clustersand clustering results can be output end-to-end while improving thegeneralization ability. As a nonparametric class method, extensive experimentsdemonstrate the superiority of \textit{GCMA} over state-of-the-art baselines.

Masked AutoEncoder for Graph Clustering without Pre-defined Cluster Number k