Foundation models pretrained on large data have demonstrated remarkable zero-shot generalization capabilities across domains. Building on the success of TabPFN for tabular data and its recent extension to time series, we investigate whether graph node classification can be effectively reformulated as a tabular learning problem. We introduce TabPFN-GN, which transforms graph data into tabular features by extracting node attributes, structural properties, positional encodings, and optionally smoothed neighborhood features. This enables TabPFN to perform direct node classification without any graph-specific training or language model dependencies. Our experiments on 12 benchmark datasets reveal that TabPFN-GN achieves competitive performance with GNNs on homophilous graphs and consistently outperforms them on heterophilous graphs. These results demonstrate that principled feature engineering can bridge the gap between tabular and graph domains, providing a practical alternative to task-specific GNN training and LLM-dependent graph foundation models.

翻译：基于大规模数据预训练的基础模型已在跨领域零样本泛化能力上展现出卓越性能。在TabPFN处理表格数据的成功及其近期在时间序列上的扩展基础上，本研究探讨能否将图节点分类问题有效重构为表格学习任务。我们提出TabPFN-GN方法，通过提取节点属性、结构特征、位置编码及可选的平滑邻域特征，将图数据转化为表格特征。这使得TabPFN无需任何图特定训练或语言模型依赖即可直接执行节点分类。在12个基准数据集上的实验表明：TabPFN-GN在同配图上与图神经网络性能相当，在异配图上则持续优于图神经网络。这些结果证明，基于原理的特征工程能够弥合表格与图领域之间的鸿沟，为任务特定的图神经网络训练和依赖大语言模型的图基础模型提供了实用替代方案。