提出多尺度消息传递机制，让图神经网络更精准分类节点。
Scale-aware Message Passing For Graph Node Classification
- 设计可感知尺度的消息传递架构，融合多尺度特征
- 在6个基准数据集上达领先性能，大图上也表现优异
- 适合研究图神经网络泛化能力或大规模图分析的学者
大多数图神经网络（GNN）仅在第一阶尺度下运行，尽管多尺度表示在图像分类等领域已被证明至关重要。本文探讨了GNN是否也能从多尺度学习中获益，而非局限于固定深度的k跳聚合。首先，我们形式化了图学习中的尺度不变性，提供了理论保证和实证证据。基于此，提出ScaleNet，一种结合有向多尺度特征聚合与自适应自环机制的尺度感知消息传递架构，在六个基准数据集（涵盖同质与异质图）上达到当前最优性能。为应对可扩展性挑战，进一步提出LargeScaleNet，将多尺度学习拓展至大规模图，在三个大规模基准上刷新记录。同时发现FaberNet的优越性主要源于多尺度特征融合。这些结果表明，尺度不变性可能是提升单阶GNN性能的重要原则。所有实验代码已公开于GitHub。
原文摘要 · Abstract (English)
Most Graph Neural Networks (GNNs) operate at the first-order scale, even though multi-scale representations are known to be crucial in domains such as image classification. In this work, we investigate whether GNNs can similarly benefit from multi-scale learning, rather than being limited to a fixed depth of $k$-hop aggregation. We begin by formalizing scale invariance in graph learning, providing theoretical guarantees and empirical evidence for its effectiveness. Building on this principle, we introduce ScaleNet, a scale-aware message-passing architecture that combines directed multi-scale feature aggregation with an adaptive self-loop mechanism. ScaleNet achieves state-of-the-art performance on six benchmark datasets, covering both homophilic and heterophilic graphs. To handle scalability, we further propose LargeScaleNet, which extends multi-scale learning to large graphs and sets new state-of-the-art results on three large-scale benchmarks. We also show that FaberNet's strength largely arises from multi-scale feature integration. Together with these state-of-the-art results, our findings suggest that scale invariance may serve as a valuable principle for improving the performance of single-order GNNs. The code for all experiments is available at \href{https://github.com/Qin87/ScaleNet/tree/iclr_scale_aware/}{this link}.
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。