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Federated GNNs sync embeddings to detect subgraph patterns across clients

作者 PulseAugur 编辑部 · [2 个来源] ·

Researchers have developed a novel framework for federated subgraph pattern detection, addressing the challenge of decentralized graph data. Their approach involves a per-step, layer-wise exchange of intermediate node representations among clients. This method aims to bridge the representation-equivalence gap that arises when graph neural networks operate on distributed data, without compromising raw features or labels. AI

影响 Enhances privacy-preserving graph analysis by enabling GNNs on distributed datasets without centralizing raw data.

排序理由 Academic paper detailing a new method for federated subgraph pattern detection.

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

Federated GNNs sync embeddings to detect subgraph patterns across clients

报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Selin Ceydeli, Rui Wang, Kubilay Atasu ·

    Federated Cross-Client Subgraph Pattern Detection

    arXiv:2605.06433v1 Announce Type: new 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 distrib…

  2. arXiv cs.LG TIER_1 English(EN) · Kubilay Atasu ·

    Federated Cross-Client Subgraph Pattern Detection

    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 c…

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