FedSuCo: Federated Graph Clustering via Riemannian Geometry-Guided Supernode Collaboration

Federated graph clustering requires learning across fragmented graph structures while keeping individual-node information local. We propose FedSuCo, which uses supernodes guided by Riemannian geometry as the unit of collaboration. Specifically, in FedSuCo, clients learn Riemannian node representations and construct supernodes through geometric renormalization, preserving coarse structural organization while retaining clustering-relevant angular similarity. Supernode representations and securely aggregated connectivity statistics jointly form a weighted global quotient graph, on which the server learns structural guidance and transfers it back to local nodes. Final federated spherical clustering aggregates only cluster-level sufficient statistics, while node representations and assignments remain local. Experiments on five real-world graph datasets against thirteen federated baselines show consistent improvements across clustering metrics.