Federated Cross-Client Subgraph Pattern Detection

• URL: http://arxiv.org/abs/2605.06433v1
• Date: Thu, 07 May 2026 15:35:37 GMT
• Title: Federated Cross-Client Subgraph Pattern Detection
• Abstract summary: Subgraph pattern detection aims to uncover complex interaction structures in graphs.<n>State-of-the-art graph neural network (GNN)-based solutions assume centralized access to the entire graph.<n>We formalize this as a structural observability problem, where subgraph patterns crossing partition boundaries become locally unidentifiable.
• Score: 3.903194329577748
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Subgraph pattern detection aims to uncover complex interaction structures in graphs. However, state-of-the-art graph neural network (GNN)-based solutions assume centralized access to the entire graph. When graphs are instead distributed across multiple parties, client-local GNN computations diverge from those of a centralized model, resulting in a representation-equivalence gap. We formalize this as a structural observability problem, where subgraph patterns crossing partition boundaries become locally unidentifiable. To bridge this gap, we propose a per-step, layer-wise embedding exchange framework in which clients synchronize intermediate node representations at each layer of the forward pass, without exposing raw features or labels. Under an extended-subgraph assumption and shared model parameters across clients, this framework recovers the same node representations as a centralized GNN over the full graph. Experiments on synthetic directed multigraphs with cycles, bicliques, and scatter-gather patterns show that embedding exchange and federated parameter aggregation are complementary rather than interchangeable: their combination recovers most of the representation gap, provided exchanged embeddings are fresh per-step rather than stale per-epoch.

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