Node4All: Learning Node Representation Beyond Datasets

2026-07-19

AI Summary

This work addresses the limited generalization of existing node representation learning methods, which typically require dataset-specific training and hyperparameter tuning. The authors propose Node4All, the first framework enabling universal node representation learning across arbitrary graphs with a single model. Built upon a Channel Graph Transformer (CGT) architecture and powered by synthetic graph-driven self-supervised learning, Node4All eliminates the need for dataset-specific fine-tuning and supports both zero-shot and in-context learning. Evaluated on 25 benchmark datasets, it achieves an average rank of 5th, significantly outperforming most baselines and surpassing current graph foundation models under one-shot and in-context settings, thereby overcoming the dataset-specific limitations of conventional approaches.

Abstract

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.

Problem

• node representation learning
• graph generalization
• dataset-specific optimization
• graph models
• reusability

Innovation

• Channel Graph Transformer
• dataset-agnostic learning
• self-supervised graph learning
• graph foundation model
• node representation learning