  Given entities and their interactions in the web data, which may have
occurred at different time, how can we find communities of entities and track
their evolution? In this paper, we approach this important task from graph
clustering perspective. Recently, state-of-the-art clustering performance in
various domains has been achieved by deep clustering methods. Especially, deep
graph clustering (DGC) methods have successfully extended deep clustering to
graph-structured data by learning node representations and cluster assignments
in a joint optimization framework. Despite some differences in modeling choices
(e.g., encoder architectures), existing DGC methods are mainly based on
autoencoders and use the same clustering objective with relatively minor
adaptations. Also, while many real-world graphs are dynamic, previous DGC
methods considered only static graphs. In this work, we develop CGC, a novel
end-to-end framework for graph clustering, which fundamentally differs from
existing methods. CGC learns node embeddings and cluster assignments in a
contrastive graph learning framework, where positive and negative samples are
carefully selected in a multi-level scheme such that they reflect hierarchical
community structures and network homophily. Also, we extend CGC for
time-evolving data, where temporal graph clustering is performed in an
incremental learning fashion, with the ability to detect change points.
Extensive evaluation on real-world graphs demonstrates that the proposed CGC
consistently outperforms existing methods.
