English(EN) Are Common Substructures Transferable? Riemannian Graph Foundation Model with Neural Vector Bundles

GAUGE模型使用黎曼几何实现可迁移图结构

作者 PulseAugur 编辑部 · [2 个来源] · 2026-06-02 07:35

研究人员推出了一款新的图基础模型GAUGE，该模型利用黎曼几何来理解可迁移结构。该框架名为神经向量丛（Neural Vector Bundle），通过局部坐标解析内在几何。GAUGE专为预训练而设计，并在零样本链接预测和图同构等任务中展现出卓越的表达能力。 AI

影响引入了一种新颖的几何方法来处理图基础模型，有望提高迁移学习能力。

排序理由该集群包含一篇详细介绍新模型和框架的学术论文。

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

报道来源 [2]

arXiv cs.AI TIER_1 English(EN) · Li Sun, Zhenhao Huang, Yiding Wang, Qin Chen, Pietro Lio, Philip S. Yu · 2026-06-03 04:00

常见子结构可迁移吗？基于神经向量丛的黎曼图基础模型

arXiv:2606.03270v1 Announce Type: cross Abstract: Foundation models have sparked a revolution via a pretraining-adaptation paradigm, with recent efforts extending this success to graphs. Unlike other modalities, graphs contain rich structural patterns, yet their structural transf…
Hugging Face Daily Papers TIER_1 English(EN) · 2026-06-02 07:35

Are Common Substructures Transferable? Riemannian Graph Foundation Model with Neural Vector Bundles

Foundation models have sparked a revolution via a pretraining-adaptation paradigm, with recent efforts extending this success to graphs. Unlike other modalities, graphs contain rich structural patterns, yet their structural transferability remains poorly understood. Prior studies…