Multi-Domain Riemannian Graph Gluing for Building Graph Foundation Models
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 a transferability measure based on geometric consistence. Extensive experiments demonstrate its superior performance across diverse graph domains. Moreover, we empirically validated GraphGlue's geometric scaling law, showing that larger quantities of datasets improve model transferability by producing a smoother manifold. Codes are available at https://github.com/RiemannGraph/GraphGlue.
69a7eee22833ff1122ff6653
Li Sun
69a7eee22833ff1122ff6654
Zhenhao Huang
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2026-03-04T09:22:06.763Z
69a7edd16cbe13094548f33f
/avatars/2e5d93521fcdf845874322a09efc8b57.svg
Zhenhao Huang
ZhenhHuang
user
ZhenhHuang
69a7eee22833ff1122ff6655
Silei Chen
69a7eee22833ff1122ff6656
Lanxu Yang
69a7eee22833ff1122ff6657
Junda Ye
69a7eee22833ff1122ff6658
Sen Su
69a7eee22833ff1122ff6659
Philip S. Yu
Multi-Domain Riemannian Graph Gluing for Building Graph Foundation Models
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 a transferability measure based on geometric consistence. Extensive experiments demonstrate its superior performance across diverse graph domains. Moreover, we empirically validated GraphGlue's geometric scaling law, showing that larger quantities of datasets improve model transferability by producing a smoother manifold. Codes are available at https://github.com/RiemannGraph/GraphGlue.
Multi-domain graph pre-training leverages Riemannian geometry to integrate knowledge across domains by merging graph datasets into a unified manifold, enabling systematic understanding of knowledge transfer through neural manifold gluing theory.
Riemannian geometry graph pre-training domain adaptation neural manifold gluing adaptive orthogonal frame graph datasets unified manifold geometric consistency EMA prototyping transferability measure geometric scaling law