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.

我们提出了一种新的深度图级聚类方法 Uniform Deep Graph Clustering（UDGC），通过将实例均匀分配到不同的聚类中心并在单位超球面上散布这些聚类，从而实现了更均匀的聚类级分布和较小的聚类崩溃问题。我们在八个著名数据集上的实证研究显示，UDGC 明显优于现有的模型。

学习超球面上的统一簇对深度图聚类

Learning Uniform Clusters on Hypersphere for Deep Graph-level Clustering

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.

本文提出了一种面向多个图形的一般图级聚类框架，名为 GLCC，它使用对比学习技术，包括实例级和聚类级联合优化表示学习，以及使用邻域感知伪标签来奖励优化表示学习的过程。实验证实，GLCC 比其他竞争基线方法表现更好。