[2603.00618] Multi-Domain Riemannian Graph Gluing for Building Graph Foundation Models

Computer Science > Machine Learning arXiv:2603.00618 (cs) [Submitted on 28 Feb 2026] Title:Multi-Domain Riemannian Graph Gluing for Building Graph Foundation Models Authors:Li Sun, Zhenhao Huang, Silei Chen, Lanxu Yang, Junda Ye, Sen Su, Philip S. Yu View a PDF of the paper titled Multi-Domain Riemannian Graph Gluing for Building Graph Foundation Models, by Li Sun and 6 other authors View PDF HTML (experimental) Abstract:Multi-domain graph pre-training integrates knowledge from diverse domains to enhance performance in the target domains, which is crucial for building graph foundation models. Despite initial success, existing solutions often fall short of answering a fundamental question: how is knowledge integrated or transferred across domains? This theoretical limitation motivates us to rethink the consistency and transferability between model pre-training and domain adaptation. In this paper, we propose a fresh Riemannian geometry perspective, whose core idea is to merge any graph dataset into a unified, smooth Riemannian manifold, enabling a systematic understanding of knowledge integration and transfer. To achieve this, our key contribution is the theoretical establishment of neural manifold gluing, which first characterizes local geometry using an adaptive orthogonal frame and then "glues" the local pieces together into a coherent whole. Building on this theory, we present the GraphGlue framework, which supports batched pre-training with EMA prototyping and provides...