Towards Adaptive Federated Graph Clustering: A Global Community-aware Contrastive Learning-based Approach
cs.LG
Submitted: 2026-08-01
Updated: 2026-08-01
Comments: Under Review
License: http://creativecommons.org/licenses/by/4.0/
The gist: 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
Terminology
Abstract
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.
Sources
- GPT-4 Technical Report
- Federated Graph Unlearning
- Federated Learning with Personalization Layers
- Federated Graph Semantic and Structural Learning
- Semi-Supervised Classification with Graph Convolutional Networks
- OpenFGL: A Comprehensive Benchmark for Federated Graph Learning
- Knowledge Graph Contrastive Learning Based on Relation-Symmetrical Structure
- A Survey of Deep Graph Clustering: Taxonomy, Challenge, Application, and Open Resource
- Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models
- Learning Transferable Visual Models From Natural Language Supervision
- Pitfalls of Graph Neural Network Evaluation
- S2FGL: Spatial Spectral Federated Graph Learning
- A Comprehensive Data-centric Overview of Federated Graph Learning
- Barlow Twins: Self-Supervised Learning via Redundancy Reduction
- Towards Effective Federated Graph Foundation Model via Mitigating Knowledge Entanglement
- FedTAD: Topology-aware Data-free Knowledge Distillation for Subgraph Federated Learning
- An Empirical Study of Graph Contrastive Learning
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