Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees

• URL: http://arxiv.org/abs/2412.16441v3
• Date: Sun, 25 May 2025 14:58:07 GMT
• Title: Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees
• Authors: Zehong Wang, Zheyuan Zhang, Tianyi Ma, Nitesh V Chawla, Chuxu Zhang, Yanfang Ye,
• Abstract summary: We propose a novel approach to cross-task generalization in graphs via task-trees.<n>We show that pretraining a graph neural network (GNN) on diverse task-trees with a reconstruction objective induces transferable knowledge.<n>This enables efficient adaptation to downstream tasks with minimal fine-tuning.
• Score: 50.78679002846741
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Foundation models are pretrained on large-scale corpora to learn generalizable patterns across domains and tasks -- such as contours, textures, and edges in images, or tokens and sentences in text. In contrast, discovering such generalities in graph-structured data, especially across heterogeneous graph tasks, remains an open challenge. To address this, we propose a novel approach to cross-task generalization in graphs via task-trees, which serve as unified learning instances aligning node-, edge-, and graph-level tasks. We theoretically analyze the stability, transferability, and generalization properties of task-trees, showing that pretraining a graph neural network (GNN) on diverse task-trees with a reconstruction objective induces transferable knowledge. This enables efficient adaptation to downstream tasks with minimal fine-tuning. To validate our framework, we introduce Graph Generality Identifier on Task-Trees (GIT), a graph foundation model that demonstrates strong performance on over 30 graphs across five domains via fine-tuning, in-context learning, and zero-shot generalization. Code and data are available at https://github.com/Zehong-Wang/GIT.

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