Masked AutoEncoder for Graph Clustering without Pre-defined Cluster
  Number k
Yuanchi Ma Hui He Zhongxiang Lei Zhendong Niu
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 Graph Clustering with
Masked Autoencoders (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.