用两套原型增强图神经网络的全局感知与抗噪能力
$P^2$GNN: Two Prototype Sets to boost GNN Performance
- 引入两套原型作为全局邻居和消息对齐基准
- 在18个数据集上提升性能，部分超越电商生产模型
- 适合需要鲁棒节点分类的工业场景
消息传递图神经网络（MP-GNNs）在用户推荐、欺诈检测等工业场景中表现优异，但面临两大挑战：过度依赖局部上下文，缺乏全局信息；以及对节点同质性假设过强，难以处理噪声邻域。为此，我们提出P²GNN，一种可即插即用的原型优化方法，通过两种方式利用原型：（1）将原型视为所有节点的通用邻居以丰富全局上下文；（2）使消息对齐至聚类原型，实现去噪。该方法适用于所有消息传递GNN，我们在18个数据集（包括自有电商业务数据集与开源数据集）上进行实验，涵盖节点推荐与节点分类任务。结果表明，P²GNN在电商业务中超越现有生产模型，并在开源数据集上取得最高平均排名，验证了其领先性。定性分析进一步支持全局上下文与局部去噪对性能提升的关键作用。
原文摘要 · Abstract (English)
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.
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