Scalable Deep Graph Clustering with Random-walk based Self-supervised Learning. (arXiv:2112.15530v1 [cs.LG])

Web-based interactions can be frequently represented by an attributed graph,
and node clustering in such graphs has received much attention lately. Multiple
efforts have successfully applied Graph Convolutional Networks (GCN), though
with some limits on accuracy as GCNs have been shown to suffer from
over-smoothing issues. Though other methods (particularly those based on
Laplacian Smoothing) have reported better accuracy, a fundamental limitation of
all the work is a lack of scalability. This paper addresses this open problem
by relating the …

arxiv clustering graph learning random self-supervised learning supervised learning