发表机构
Tianjin University（天津大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现有图基础模型忽略可迁移传播知识单元与传播模式异质性的局限，本文提出ProGFM，通过传播关系原型库学习跨域可迁移传播知识，在多跨域场景下实现更优泛化性能。
AI 中文摘要
图基础模型（Graph Foundation Models, GFMs）是近期兴起的、可实现跨领域知识迁移的有前景范式，与传统面向领域内场景的图学习方法不同，GFMs旨在学习可泛化到未见图领域的可迁移知识。但与语言或视觉数据不同，图缺乏内在统一的表示单元（如语言中的token、视觉中的patch），这给识别构建GFM所需的可迁移知识单元带来挑战。现有GFMs主要通过特征对齐和结构对齐缓解领域差异，却忽略了对图数据底层可迁移知识单元的探索；且这些方法在消息传递中通常依赖固定传播机制，未考虑传播模式的异质性——不同边在不同特征维度上可能呈现不同传播模式。为解决上述局限，本文提出传播感知图基础模型（Propagation-aware Graph Foundation Model, ProGFM），将边与特征维度间的传播关系视为可迁移知识单元，通过传播关系原型库学习跨域可迁移传播知识，实现未见图领域的自适应信息聚合。在多种跨域迁移场景下开展的大量实验表明，ProGFM具备强大的跨域知识迁移能力，相较于现有方法表现出更优的泛化性能。
英文摘要
Graph Foundation Models (GFMs) have recently emerged as a promising paradigm for enabling knowledge transfer across diverse domains. Unlike traditional graph learning methods that are typically designed for in-domain settings, GFMs aim to learn transferable knowledge that can generalize to unseen graph domains. However, unlike language or visual data, graphs lack intrinsic and unified representation units, such as tokens in language and patches in vision, making it challenging to identify transferable knowledge units for building graph foundation models. Existing graph foundation models mainly focus on mitigating domain discrepancies through feature alignment and structure alignment, while overlooking the exploration of transferable knowledge units underlying graph data. Moreover, these methods generally rely on fixed propagation mechanisms during message passing, overlooking the heterogeneity in propagation patterns, as different edges may exhibit distinct propagation patterns for different feature dimensions. To address these limitations, we propose a Propagation-aware Graph Foundation Model (ProGFM), which regards the propagation relationships between edges and feature dimensions as transferable knowledge units. Through a propagation relationship prototype bank, ProGFM learns cross-domain transferable propagation knowledge, enabling adaptive information aggregation in unseen graph domains. Extensive experiments across various cross-domain transfer scenarios demonstrate that ProGFM possesses strong cross-domain knowledge transfer capability and exhibits superior generalization performance compared with existing methods.
