Towards Adaptive Federated Graph Clustering: A Global Community-aware Contrastive Learning-based Approach

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 graph