Graph clustering, a fundamental and challenging task in graph mining, aims to
classify nodes in a graph into several disjoint clusters. In recent years,
graph contrastive learning (GCL) has emerged as a dominant line of research in
graph clustering and advances the new state-of-the-art. However, GCL-based
methods heavily rely on graph augmentations and contrastive schemes, which may
potentially introduce challenges such as semantic drift and scalability issues.
Another promising line of research involves the adoption of modularity
maximization, a popular and effective measure for community detection, as the
guiding principle for clustering tasks. Despite the recent progress, the
underlying mechanism of modularity maximization is still not well understood.
In this work, we dig into the hidden success of modularity maximization for
graph clustering. Our analysis reveals the strong connections between
modularity maximization and graph contrastive learning, where positive and
negative examples are naturally defined by modularity. In light of our results,
we propose a community-aware graph clustering framework, coined MAGI, which
leverages modularity maximization as a contrastive pretext task to effectively
uncover the underlying information of communities in graphs, while avoiding the
problem of semantic drift. Extensive experiments on multiple graph datasets
verify the effectiveness of MAGI in terms of scalability and clustering
performance compared to state-of-the-art graph clustering methods. Notably,
MAGI easily scales a sufficiently large graph with 100M nodes while
outperforming strong baselines.

本研究探讨了模块性最大化方法在图聚类中的潜在成功，将模块性最大化与图对比学习联系起来，提出了一种基于社区感知的图聚类框架 MAGI，通过利用模块性最大化作为对比预训练任务来有效地揭示图中社区的潜在信息，同时避免了语义漂移问题。广泛实验证实了 MAGI 在多个图数据集上的可扩展性和聚类性能，表现优于现有的图聚类方法，并且在大规模图上具有优越的性能。