Computer Science > Machine Learning
arXiv:2603.09195 (cs)
[Submitted on 10 Mar 2026]
Title:$P^2$GNN: Two Prototype Sets to boost GNN Performance
View a PDF of the paper titled $P^2$GNN: Two Prototype Sets to boost GNN Performance, by Arihant Jain and 3 other authors
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Abstract:Message Passing Graph Neural Networks (MP-GNNs) have garnered attention for addressing various industry challenges, such as user recommendation and fraud detection. However, they face two major hurdles: (1) heavy reliance on local context, often lacking information about the global context or graph-level features, and (2) assumption of strong homophily among connected nodes, struggling with noisy local neighborhoods. To tackle these, we introduce $P^2$GNN, a plug-and-play technique leveraging prototypes to optimize message passing, enhancing the performance of the base GNN model. Our approach views the prototypes in two ways: (1) as universally accessible neighbors for all nodes, enriching global context, and (2) aligning messages to clustered prototypes, offering a denoising effect. We demonstrate the extensibility of our proposed method to all message-passing GNNs and conduct extensive experiments across 18 datasets, including proprietary e-commerce datasets and open-source datasets, on node recommendation and node classification tasks. Results show that $P^2$GNN outperforms production models in e-commerce and achieves the top average rank on open-source datasets, establishing it as a leading approach. Qualitative analysis supports the value of global context and noise mitigation in the local neighborhood in enhancing performance.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2603.09195 [cs.LG] |
| (or arXiv:2603.09195v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2603.09195
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View a PDF of the paper titled $P^2$GNN: Two Prototype Sets to boost GNN Performance, by Arihant Jain and 3 other authors
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