Dynamic Spectral Federated Graph-Level Clustering

Federated graph clustering (FGC) aims to partition distributed graphs into different groups while preserving data privacy. Existing FGC methods focus on spatial-domain information aggregation, while how to use expressive spectral-domain signals of graphs for FGC still remains unexplored. Main challenges are twofold: (1) Collecting and integrating semantic signals that fully express the critical patterns of graphs; (2) Obtaining spectral consensus while preserving local properties of clients. To address these challenges, we propose dynamic spectral federated graph-level clustering (DySFGC). DySFGC designs learnable polynomial filters to capture abundant graph signals and employs spectral contrastive learning and reconstruction to explore the latent semantic information. Besides, DySFGC develops dynamic spectral consensus, which updates the consensus via frequency-specific channels and conducts the dynamic updating process by evaluating the discrepancies between the server and clients. Extensive experiments on fifteen datasets demonstrate the superiority of DySFGC over eleven baselines.