  This paper studies the problem of graph-level clustering, which is a novel
yet challenging task. This problem is critical in a variety of real-world
applications such as protein clustering and genome analysis in bioinformatics.
Recent years have witnessed the success of deep clustering coupled with graph
neural networks (GNNs). However, existing methods focus on clustering among
nodes given a single graph, while exploring clustering on multiple graphs is
still under-explored. In this paper, we propose a general graph-level
clustering framework named Graph-Level Contrastive Clustering (GLCC) given
multiple graphs. Specifically, GLCC first constructs an adaptive affinity graph
to explore instance- and cluster-level contrastive learning (CL).
Instance-level CL leverages graph Laplacian based contrastive loss to learn
clustering-friendly representations while cluster-level CL captures
discriminative cluster representations incorporating neighbor information of
each sample. Moreover, we utilize neighbor-aware pseudo-labels to reward the
optimization of representation learning. The two steps can be alternatively
trained to collaborate and benefit each other. Experiments on a range of
well-known datasets demonstrate the superiority of our proposed GLCC over
competitive baselines.
