Generalizing Graph Foundation Models via Hyperbolic Retrieval-Augmented Generation

• URL: http://arxiv.org/abs/2606.03307v2
• Date: Wed, 03 Jun 2026 03:07:00 GMT
• Title: Generalizing Graph Foundation Models via Hyperbolic Retrieval-Augmented Generation
• Abstract summary: Graph foundation models (GFMs) emerged as a dominant paradigm in graph representation learning.<n>We propose a framework designed to enhance the generalization capabilities of GFMs.<n>Experiments on multiple graph benchmarks demonstrate significant improvements in the zero-shot setting.
• Score: 36.1292155308597
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Graph foundation models (GFMs) emerged as a dominant paradigm in graph representation learning by leveraging large-scale pre-training for cross-domain inference. However, the parameterized knowledge encoded within these models is insufficient to cope with distribution shifts, limiting their generalization ability. To mitigate this issue, retrieval-augmented generation (RAG) has been introduced to incorporate external knowledge at inference time. Nevertheless, existing RAG frameworks operating in Euclidean space suffer from a fundamental geometric limitation: the polynomial volume growth of Euclidean space is inherently mismatched with the tree-structured external knowledge bases. This mismatch leads to the loss of semantic granularity in retrieval and gives rise to the hubness phenomenon.To address this limitation, we propose a Hyperbolic Retrieval-Augmented Generation (HyRAG) framework designed to enhance the generalization capabilities of GFMs. Specifically, the introduced Hyperbolic Knowledge Indexing module retains the tree-like hierarchies of the external knowledge base by modeling them within hyperbolic space. The Multi-granularity Retrieval module then provides GFMs with the global semantic anchors and local semantic nuances through coarse-grained and fine-grained knowledge retrieval, respectively. Finally, the Dual-path Fusion module achieves effective knowledge integration for graph tasks at both the feature and structural levels. Experiments on multiple graph benchmarks demonstrate significant improvements in the zero-shot setting, highlighting the generalization of our method for robust GFMs inference.

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