Learning Uniform Clusters on Hypersphere for Deep Graph-level Clustering
Mengling Hu Chaochao Chen Weiming Liu Xinyi Zhang Xinting Liao Xiaolin Zheng
Graph clustering has been popularly studied in recent years. However, most
existing graph clustering methods focus on node-level clustering, i.e.,
grouping nodes in a single graph into clusters. In contrast, graph-level
clustering, i.e., grouping multiple graphs into clusters, remains largely
unexplored. Graph-level clustering is critical in a variety of real-world
applications, such as, properties prediction of molecules and community
analysis in social networks. However, graph-level clustering is challenging due
to the insufficient discriminability of graph-level representations, and the
insufficient discriminability makes deep clustering be more likely to obtain
degenerate solutions (cluster collapse). To address the issue, we propose a
novel deep graph-level clustering method called Uniform Deep Graph Clustering
(UDGC). UDGC assigns instances evenly to different clusters and then scatters
those clusters on unit hypersphere, leading to a more uniform cluster-level
distribution and a slighter cluster collapse. Specifically, we first propose
Augmentation-Consensus Optimal Transport (ACOT) for generating uniformly
distributed and reliable pseudo labels for partitioning clusters. Then we adopt
contrastive learning to scatter those clusters. Besides, we propose Center
Alignment Optimal Transport (CAOT) for guiding the model to learn better
parameters, which further promotes the cluster performance. Our empirical study
on eight well-known datasets demonstrates that UDGC significantly outperforms
the state-of-the-art models.