# Masked AutoEncoder for Graph Clustering without Pre-defined Cluster Number k. (arXiv:2401.04741v1 [cs.LG])


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

[Read Full Article](http://arxiv.org/abs/2401.04741)