重新审视图聚类中的模块度最大化：一种对比学习视角
Revisiting Modularity Maximization for Graph Clustering: A Contrastive Learning Perspective
AI总结：
本研究揭示模块度最大化与图对比学习的内在关联，提出以模块度最大化为对比预训练任务的社区感知图聚类框架MAGI，兼具优异聚类性能与超大规模图可扩展性。
摘要：
图聚类是图挖掘中一项基础且具有挑战性的任务，旨在将图中的节点划分为若干互不相交的簇。近年来，图对比学习（GCL）已成为图聚类领域的主流研究方向，并推动了最新最优性能的突破。然而，基于GCL的方法严重依赖图增强和对比策略，这可能会带来语义漂移、可扩展性不足等问题。另一类有前景的研究方向是采用模块度最大化——一种广泛使用且有效的社区发现指标——作为聚类任务的指导原则。尽管近年来取得了一定进展，但模块度最大化的内在机制仍未被充分理解。在本研究中，我们深入探究了模块度最大化在图聚类中取得成功的深层原因。我们的分析揭示了模块度最大化与图对比学习之间的紧密联系：正例和负例可由模块度自然定义。基于这一发现，我们提出了一个社区感知的图聚类框架，命名为MAGI，该框架将模块度最大化作为对比预训练任务，既能有效挖掘图中社区的潜在信息，又能避免语义漂移问题。我们在多个图数据集上开展了大量实验，结果验证了与当前最优的图聚类方法相比，MAGI在可扩展性和聚类性能上均具备有效性。值得注意的是，MAGI可轻松扩展至包含1亿个节点的超大规模图，同时性能优于多个强基线模型。
英文摘要：
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.
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