Graph Clustering with Masked AutoEncoders
Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
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Keywords: Graph Clustering, Masked Graph Autoencoder, Fusion Network
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Abstract: Graph clustering algorithms with autoencoder structures have recently gained popularity due to their efficient performance and low training cost. However, for existing graph autoencoder clustering algorithms based on GCN or GAT, not only do they lack good generalization ability, but also the number of clusters clustered by such autoencoder models is difficult to determine automatically. To solve this problem, we propose a new framework called \textit{G}raph \textit{C}lustering with \textit{M}asked \textit{A}utoencoders (\textit{GCMA}). It employs our designed fusion autoencoder based on the graph masking method for the fusion coding of graph. It introduces our improved density-based clustering algorithm as a second decoder while decoding with multi-target reconstruction. By decoding the mask embedding, our model can capture more generalized and comprehensive knowledge. The number of clusters and clustering results can be output end-to-end while improving the generalization ability. As a nonparametric class method, extensive experiments demonstrate the superiority of \textit{GCMA} over state-of-the-art baselines.
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Submission Number: 3432
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