发表机构
Sun Yat-sen University（中山大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对联邦图聚类中预定义聚类基数不现实和社区间分离不完整的问题，提出基于全局社区感知对比学习的自适应框架AdaFGC，在八个基准数据集上优于现有基线。
AI 中文摘要
联邦图学习（FGL）使多个客户端能够在不共享其私有图数据的情况下协作训练图模型，为从分布式图存储库中挖掘知识提供了一种有前景的范式。虽然大多数现有的FGL方法侧重于监督任务，但现实世界的图通常规模庞大且无标签，这使得联邦图聚类成为一个重要但尚不成熟的研究方向。值得注意的是，由于客户端之间固有的子图异质性，该任务尤其具有挑战性，这导致了客户端特定的社区结构。在本工作中，我们识别了现有联邦图聚类方法的两个关键局限性：（1）不切实际的预定义聚类基数假设和（2）不完整的社区间分离。为了解决这些挑战，我们提出了AdaFGC，一种基于全局社区感知对比学习的自适应联邦图聚类框架。AdaFGC引入了一组过完备的全局社区锚点来建模全局社区结构，并通过跨客户端锚点细化自适应地估计聚类基数。此外，它采用了一种全局社区感知的对比学习方案，使用共享锚点作为对比原型，在客户端之间显式地强制实施社区级吸引和排斥，并辅以节点级和拓扑级目标来稳定局部表示。在八个基准数据集上的大量实验表明，AdaFGC在多个聚类指标上持续优于现有的监督和无监督FGL基线。
英文摘要
Federated graph learning (FGL) enables multiple clients to collaboratively train graph models without sharing their private graph data, providing a promising paradigm for mining knowledge from distributed graph repositories. While most existing FGL methods focus on supervised tasks, real-world graphs are often massive and unlabeled, making federated graph clustering an important yet still immature research direction. Notably, this task is particularly challenging due to the inherent subgraph heterogeneity across clients, which leads to client-specific community structures. In this work, we identify two critical limitations in existing federated graph clustering methods: (1) unrealistic pre-defined cluster cardinality assumptions and (2) incomplete inter-community separation. To address these challenges, we propose AdaFGC, an Adaptive Federated graph clustering framework based on Global community-aware Contrastive learning. AdaFGC introduces an over-complete set of global community anchors to model the global community structure and adaptively estimate clustering cardinality via cross-client anchor refinement. In addition, it employs a global community-aware contrastive learning scheme that uses the shared anchors as contrastive prototypes to explicitly enforce community-level attraction and repulsion across clients, complemented by node-level and topology-level objectives that stabilize local representations. Extensive experiments on eight benchmark datasets demonstrate that AdaFGC consistently outperforms existing supervised and unsupervised FGL baselines across multiple clustering metrics.
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