English(EN) Surprisingly Simple and Effective Multi-Domain Graph Foundation Model through Graph-to-Table Alignment

新的GTAlign框架简化了图基础模型

作者 PulseAugur 编辑部 · [3 个来源] · 2026-07-13 10:35

研究人员推出了一种新颖的文本无关图基础模型（GFM）框架GTAlign。该方法旨在弥合图拓扑与表格表示空间之间的差距，使GFM能够更有效地捕获结构图信息。GTAlign预训练图编码器，并使用社区引导的伪标签持续预训练来增强理解能力，在节点和图分类任务中表现优于现有方法。 AI

影响引入了一种新颖的文本无关图基础模型方法，有望提高图基AI任务的性能和可访问性。

排序理由该集群包含两篇相同的arXiv预印本，详细介绍了一个关于图基础模型的新研究论文。

AI 生成摘要 · Google Gemini · 来自 3 个来源。我们如何撰写摘要 →

报道来源 [3]

arXiv cs.LG TIER_1 English(EN) · Chunyu Hu, Tianyin Liao, Ge Lan, Xingxuan Zhang, Jianxin Li, Peng Cui, Ziwei Zhang · 2026-07-14 04:00

Surprisingly Simple and Effective Multi-Domain Graph Foundation Model through Graph-to-Table Alignment

arXiv:2607.11374v1 Announce Type: new Abstract: Graph Foundation Models (GFMs) have emerged as a promising paradigm for learning transferable representations across diverse graph domains. Recent advancements in GFMs have been largely dominated by two paradigms: Graph Neural Netwo…
arXiv cs.LG TIER_1 English(EN) · Ziwei Zhang · 2026-07-13 10:35

通过图到表对齐实现出人意料的简单有效的多领域图基础模型

Graph Foundation Models (GFMs) have emerged as a promising paradigm for learning transferable representations across diverse graph domains. Recent advancements in GFMs have been largely dominated by two paradigms: Graph Neural Network and Large Language Model (LLM) based methods.…
Hugging Face Daily Papers TIER_1 English(EN) · 2026-07-13 10:35

Surprisingly Simple and Effective Multi-Domain Graph Foundation Model through Graph-to-Table Alignment

Graph Foundation Models (GFMs) have emerged as a promising paradigm for learning transferable representations across diverse graph domains. Recent advancements in GFMs have been largely dominated by two paradigms: Graph Neural Network and Large Language Model (LLM) based methods.…