发表机构
KAIST; Seoul National University（韩国科学技术院; 首尔国立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对多数节点表示学习方法依赖特定数据集训练的问题，提出Node4All，基于通道图变换器和自监督学习构建，能跨任意图数据集泛化，在节点分类任务中表现出色，实现可复用性且实践效果好。
AI 中文摘要
节点表示学习发展迅速，但多数现有方法依赖于每个数据集的训练和超参数调整。这种特定于数据集的优化源于设计可跨不同图数据集泛化的可复用图模型的困难。本文介绍了Node4All，一种无需任何特定于数据集的优化即可应用于任意图数据集的节点表示学习器。它基于两个互补思想构建。在架构层面，引入通道图变换器（CGT），能以单一固定参数化处理任意图数据集。在学习层面，提出基于一系列合成图的自监督学习。通过广泛评估，Node4All在25个基准测试的节点分类任务中与21个基线方法竞争，排名第5，还支持一次性和上下文学习，优于近期图基础模型。这些结果表明Node4All不仅实现了跨任意图数据集的可复用性，在实践中也是有效的解决方案。
英文摘要
Node representation learning has advanced rapidly, yet most existing methods rely on per-dataset training and hyperparameter tuning. This dataset-specific optimization comes from the difficulty of designing reusable graph models that generalize across diverse graph datasets. In this work, we introduce Node4All, a node representation learner applicable to arbitrary graph datasets without any dataset-specific optimization. Node4All is built on two complementary ideas. At the architectural level, we introduce the Channel Graph Transformer (CGT), which enables a single fixed parameterization to process arbitrary graph datasets. At the learning level, we propose a self-supervised learning based on a series of synthetic graphs. Together, these components enable generalization beyond individual datasets, which is infeasible with existing architectures and learning frameworks. We extensively evaluate Node4All on node classification across 25 benchmarks against 21 baselines, covering both supervised and self-supervised methods. Despite all baselines being trained and optimized for each dataset, a single Node4All, applied uniformly across the datasets, achieves a competitive ranking of 5th among 21 baselines. Moreover, Node4All supports one-shot and in-context learning with an appropriate predictor and outperforms recent graph foundation models (GFMs) in these settings. These results demonstrate that Node4All not only achieves reusability across arbitrary graph datasets, but also remains an effective solution in practice. Code and model checkpoints are available in https://github.com/dooho00/node4all.
CommentsAccepted to KDD 2026
