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。
在 Hugging Face Daily Papers 阅读 →
AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →
完整方法见我们的编辑标准。
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…
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.…
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.…