English(EN) Learning Graph Foundation Models on Riemannian Graph-of-Graphs

新的图基础模型学习多尺度表示

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-11 05:09

研究人员推出了一种新颖的图基础模型 R-GFM，该模型利用黎曼图之图方法来解决现有模型的局限性。与使用固定跳子图采样的先前方法不同，R-GFM 将结构尺度作为主要元素进行建模，构建多尺度图并从黎曼流形中学习表示。据报道，这种新架构可减少结构域泛化误差，并在下游任务上取得了最先进的性能，相对提高了 49%。 AI

影响引入了一种新的图基础模型架构，通过适应结构尺度来提高各种图任务的性能。

排序理由该集群包含一篇详细介绍新颖模型架构及其性能的新学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Xike Xie · 2026-05-11 05:09

黎曼图-图上的学习图基础模型

Graph foundation models (GFMs), pretrained on massive graph data, have transformed graph machine learning by supporting general-purpose reasoning across diverse graph tasks and domains. Existing GFMs pretrained with fixed-hop subgraph sampling impose a fixed receptive field, caus…

本文如何被排名

报道来源 [1]

黎曼图-图上的学习图基础模型

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