首次从可迁移性角度系统分类图基础模型，厘清发展路径。
Towards Graph Foundation Models: A Transferability Perspective
- 按应用范围与知识迁移方式构建首个图基础模型分类体系
- 揭示领域特定与通用模型在跨域迁移中的核心差异
- 为提升图模型泛化能力提供结构化研究指引
近年来，图基础模型（GFMs）因其在多种图领域和任务中具备泛化潜力而受到广泛关注。部分工作聚焦于领域特定的GFMs，旨在解决特定领域内的多样化任务；另一些则致力于构建通用型GFMs，将领域特定模型的能力扩展至多领域。无论何种类型，跨域与跨任务的可迁移性至关重要。然而，由于图数据在结构、特征和分布上的差异，实现强可迁移性仍面临重大挑战。迄今尚无系统性研究从可迁移性视角分析GFMs。为此，本文提出首个全面的分类体系，基于应用范围（领域特定 vs. 通用）与知识获取及迁移方法，对现有GFMs进行分类与分析，系统梳理当前进展，并指明未来提升跨图数据集与任务泛化能力的潜在路径。旨在阐明当前GFM研究格局，激发未来发展方向。
原文摘要 · Abstract (English)
In recent years, Graph Foundation Models (GFMs) have gained significant attention for their potential to generalize across diverse graph domains and tasks. Some works focus on Domain-Specific GFMs, which are designed to address a variety of tasks within a specific domain, while others aim to create General-Purpose GFMs that extend the capabilities of domain-specific models to multiple domains. Regardless of the type, transferability is crucial for applying GFMs across different domains and tasks. However, achieving strong transferability is a major challenge due to the structural, feature, and distributional variations in graph data. To date, there has been no systematic research examining and analyzing GFMs from the perspective of transferability. To bridge the gap, we present the first comprehensive taxonomy that categorizes and analyzes existing GFMs through the lens of transferability, structuring GFMs around their application scope (domain-specific vs. general-purpose) and their approaches to knowledge acquisition and transfer. We provide a structured perspective on current progress and identify potential pathways for advancing GFM generalization across diverse graph datasets and tasks. We aims to shed light on the current landscape of GFMs and inspire future research directions in GFM development.
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