English(EN) Beyond Feature and Structure Alignment: Learning Transferable Propagation Knowledge for Graph Foundation Models

新ProGFM模型增强图基础模型知识迁移能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-03 04:00

研究人员推出了一种新颖的、具有传播感知能力的图基础模型ProGFM，旨在增强跨不同图领域的知识迁移。与以往侧重于特征和结构对齐的模型不同，ProGFM将边和特征维度之间的可迁移传播关系识别为关键知识单元。这种方法允许在新图领域中进行自适应信息聚合，在跨领域迁移场景中展现出卓越的泛化性能。 AI

影响这项研究有望提高基于图的AI模型在不同数据集和应用中的适应性和泛化能力。

排序理由该集群包含一篇详细介绍新模型和方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Yi Wang, Jitao Zhao, Di Jin, Dongxiao He · 2026-08-03 04:00

超越特征与结构对齐：为图基础模型学习可迁移的传播知识

arXiv:2607.28980v1 Announce Type: new Abstract: 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 …