Learning Graph Foundation Models on Riemannian Graph-of-Graphs

• URL: http://arxiv.org/abs/2605.09993v1
• Date: Mon, 11 May 2026 05:09:16 GMT
• Title: Learning Graph Foundation Models on Riemannian Graph-of-Graphs
• Abstract summary: Graph foundation models (GFMs) are pretrained on massive graph data to support general-purpose reasoning.<n>We propose R-GFM, which treats structural scale as a first-class citizen in modeling.<n>R-GFM constructs a multi-scale GoG over-sampled subgraphs at different hop distances and learns geometry-adaptive representations.
• Score: 18.179624610878744
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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, causing scale mismatch on diverse tasks, which often require heterogeneous and unknown structural contexts beyond a fixed sampling scale. We propose R-GFM, a Riemannian Graph-of-Graphs (GoG) based foundation model, that treats structural scale as a first-class citizen in modeling. R-GFM constructs a multi-scale GoG over-sampled subgraphs at different hop distances and learns geometry-adaptive representations from Riemannian manifolds. Theoretical analysis shows that R-GFM reduces structural domain generalization error compared to fixed-scale GFMs. Experiments on various datasets demonstrate that R-GFM achieves state-of-the-art performance, with up to a 49% relative improvement on downstream tasks. Our code is available at https://github.com/USTC-DataDarknessLab/R-GFM.

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