发表机构
Georgia Institute of Technology（佐治亚理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过构建平行特征变体，发现GNN架构在异质性下的性能增益依赖于节点表示，架构与表示不可独立评估，差异敏感性是主要效应。
AI 中文摘要
图神经网络（GNNs）在同质图上表现良好，但在异质设置中却面临挑战，其中连接的节点往往携带不同的标签。现有评估通常在固定的节点特征表示下比较架构，这使得关于异质性鲁棒性的结论在输入表示变化时是否保持稳定仍不清楚。我们通过构建两个大规模异质基准（Roman-Empire和Amazon-Ratings）的平行特征变体来解决这一问题，将每个图与从静态fastText向量到上下文Transformer嵌入的表示配对，并评估七种GNN架构在这些表示上的表现。我们发现表示的影响因架构而异：在Roman-Empire上，上下文增益从GCN-sep的2.38个百分点到GAT的13.67个百分点不等，H2GCN获得8.77个百分点的增益。在Amazon-Ratings上，节点文本仅限于短产品标题，GAT从fastText到MPNet提升了6.78个百分点，而GCN-sep仅变化了0.20个百分点。这些结果表明，架构性能取决于节点表示：相同的表示变化在不同架构上可能产生不同幅度的性能增益，因此架构和表示不能被视为独立的评估因素。对这两个基准的秩相关分析进一步表明，架构的相对排序在表示间保持高度稳定，从而将差异敏感性而非排序不稳定性确定为主要效应。
英文摘要
Graph Neural Networks (GNNs) perform well on homophilic graphs but struggle in heterophilic settings, where connected nodes often carry dissimilar labels. Existing evaluations typically compare architectures under a fixed node-feature representation, leaving unclear whether conclusions about heterophily robustness remain stable as the input representation changes. We address this question by constructing parallel feature variants of two large-scale heterophilic benchmarks, Roman-Empire and Amazon-Ratings, pairing each graph with representations ranging from static fastText vectors to contextual Transformer embeddings and evaluating seven GNN architectures across these representations. We find that the effect of representation varies across architectures: on Roman-Empire, the contextual gain ranges from 2.38 percentage points for GCN-sep to 13.67 points for GAT, with H2GCN gaining 8.77 points. On Amazon-Ratings, where node text is limited to short product titles, GAT improves by 6.78 points from fastText to MPNet, while GCN-sep changes by only 0.20 points. These results show that architectural performance is conditional on node representation: the same representation change can produce different magnitudes of performance gain across architectures, so architecture and representation cannot be treated as independent evaluation factors. A rank-correlation analysis on these two benchmarks further shows that the relative ordering of architectures remains highly stable across representations, isolating differential sensitivity, rather than ranking instability, as the primary effect.
CommentsAccepted to Learning on Graphs Conference 2026
