发现节点特征与图结构对齐是图池化有效的关键
The Role of Node Features in Graph Pooling
- 提出节点特征需与图拓扑对齐才能有效池化
- 实验证明满足条件时池化可提升分类性能
- 适合研究图神经网络泛化能力的学者
图池化常用于图分类任务，但其性能提升往往微弱或不一致。本文通过分析节点特征与图拓扑的交互作用，揭示池化操作依赖于与图结构高度对齐的节点特征——这一条件在实际网络中常被忽视且无法保证。我们形式化了实现有效池化的节点特征基本要求，并提出一种特征质量的量化度量方法。实证研究表明，当满足这些条件时，池化能在适当数据集上带来性能提升。
原文摘要 · Abstract (English)
Graph pooling is commonly applied in graph classification, yet its empirical gains over standard WL-1 expressive GNNs are often marginal or inconsistent. We study this gap by analysing the interaction between node features and graph topology and their effect on pooling objectives. Our analysis reveals that pooling operators require node features that are well-aligned with the graph's topology -- a condition often overlooked and not guaranteed in empirical networks. We formalise fundamental requirements for node features to enable effective pooling, and introduce a quantitative measure of feature quality. Our empirical evaluation shows that, when these requirements are satisfied, pooling can be beneficial and improve performance on appropriate datasets.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。