将图数据转为表格格式，用TabPFN实现高效节点分类
Can TabPFN Compete with GNNs for Node Classification via Graph Tabularization?
- 通过提取节点属性与结构特征，把图数据转化为表格形式
- 在12个基准数据集上，对异质图表现优于GNNs
- 无需图神经网络训练，适合快速部署的图学习任务
预训练的基础模型在大规模数据上展现出出色的零样本泛化能力。基于TabPFN在表格数据上的成功及其向时间序列的扩展，我们探究是否可将图节点分类问题重新表述为表格学习问题。提出TabPFN-GN，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，将图数据转换为表格表示，使TabPFN能直接进行节点分类，无需任何图专用训练或语言模型依赖。在12个基准数据集上的实验表明，TabPFN-GN在同质图上表现与GNN相当，在异质图上持续优于GNN。结果表明，合理的特征工程可弥合表格与图领域间的差距，为任务特定的GNN训练和依赖大语言模型的图基础模型提供实用替代方案。
原文摘要 · Abstract (English)
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.
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