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

In this work, we study the problem of partitioning a set of graphs into
different groups such that the graphs in the same group are similar while the
graphs in different groups are dissimilar. This problem was rarely studied
previously, although there have been a lot of work on node clustering and graph
classification. The problem is challenging because it is difficult to measure
the similarity or distance between graphs. One feasible approach is using graph
kernels to compute a similarity matrix for the graphs and then performing
spectral clustering, but the effectiveness of existing graph kernels in
measuring the similarity between graphs is very limited. To solve the problem,
we propose a novel method called Deep Graph-Level Clustering (DGLC). DGLC
utilizes a graph isomorphism network to learn graph-level representations by
maximizing the mutual information between the representations of entire graphs
and substructures, under the regularization of a clustering module that ensures
discriminative representations via pseudo labels. DGLC achieves graph-level
representation learning and graph-level clustering in an end-to-end manner. The
experimental results on six benchmark datasets of graphs show that our DGLC has
state-of-the-art performance in comparison to many baselines.

本文提出了一种叫做 Deep Graph-Level Clustering (DGLC) 的算法，通过图同构网络学习图的表示来解决图的相似性度量问题和图的划分问题，并在六个基准数据集上展示出了其与现有算法相比具有领先的性能。