退化但并非完全无效：基于位置编码的可变形图神经网络
Degraded but Not Entirely Ineffective: PE-Based Deformable Graph Neural Networks
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中文总结 AI 辅助
针对传统GNN在深度、长依赖、固定邻域和异配图上的多重缺陷，提出基于位置编码的可变形聚合模块PEBDSAM及简化版PEBSAM，通过位置空间可变形机制补充一阶邻居信息，即插即用于多种GNN，在多个同配和异配数据集上取得良好效果。
中文摘要 AI 辅助
许多现实场景可以用图结构数据来表示。然而，传统的基于一阶邻居传递消息的图神经网络（GNN）长期以来面临着几个基本矛盾：增加深度导致过平滑，长距离依赖导致过度压缩，固定邻域限制了感受野，并且在异配图上，拓扑邻居成为噪声源。尽管许多工作已经分别解决了这些问题，但很少有机制能同时缓解所有这些挑战。为了解决上述问题，我们提出了一种基于位置编码的可变形空间聚合模块（PEBDSAM），一步解决所有问题。具体来说，我们在位置空间利用可变形机制来识别相关节点，以补充GNN原始的的一阶邻居信息，使传统GNN能够适应异配场景。通过诊断实验，我们获得了几个主要发现：当前的偏移量没有任何效果；随后，我们分析了偏移失效的原因以及为什么即使偏移失效后模型性能仍然提升，指出了未来的研究方向。基于这些诊断实验，我们精简了原始的PEBDSAM，得到了一个简化版本，称为基于位置编码的空间聚合模块（PEBSAM）。此外，我们提出了PEBSAM-Speed以适应大型数据集。最后，我们将该模块设计为即插即用，并将其应用于GCN、GAT、GIN和GraphSAGE，在三个同配数据集和六个异配图数据集上取得了理想的结果。
英文摘要
Many real-world scenarios can be represented using graph-structured data. However, traditional GNNs that transmit messages based on first-order neighbors have long faced several fundamental contradictions: increasing depth leads to over-smoothing, long-range dependencies cause over-compression, fixed neighborhoods restrict the receptive field, and on heterophilous graphs, topological neighbors become a source of noise. Although many works have addressed these issues individually, few mechanisms can simultaneously alleviate all of these challenges. To address the aforementioned problems, we propose a Position Encoding-Based Deformable Spatial Aggregation Module (PEBDSAM) that solves them all in one step. Specifically, we utilize a deformable mechanism in the position space to identify relevant nodes to supplement the original first-order neighbor information of GNNs, allowing traditional GNNs to adapt to heterophilous scenarios. Through diagnostic experiments, we obtained several major findings: current offsets fail to have any effect; subsequently, we analyzed the causes of offset failure and why model performance still improves even after offset failure, pointing out future research directions. Based on these diagnostic experiments, we streamlined the original PEBDSAM, resulting in a simplified version, which we call the Position Encoding-Based Spatial Aggregation Module (PEBSAM). In addition, we propose a PEBSAM-Speed to adapt to large datasets. Finally, we designed the module to be plug-and-play and applied it to GCN, GAT, GIN, and GraphSAGE, achieving desirable results on three homophilous datasets and six heterophilous graph datasets.
发表机构
- Henan Polytechnic University（河南理工大学）
机构由 AI 辅助整理，请以论文原文为准。
