RAG4GFM: Bridging Knowledge Gaps in Graph Foundation Models through Graph Retrieval Augmented Generation
Xingliang Wang, Zemin Liu, Junxiao Han, Shuiguang Deng
Graph Foundation Models (GFMs) have demonstrated remarkable potential across graph learning tasks but face significant challenges in knowledge updating and reasoning faithfulness. To address these issues, we introduce the Retrieval-Augmented Generation (RAG) paradigm for GFMs, which leverages graph knowledge retrieval. We propose RAG4GFM, an end-to-end framework that seamlessly integrates multi-level graph indexing, task-aware retrieval, and graph fusion enhancement. RAG4GFM implements a hierarchical graph indexing architecture, enabling multi-granular graph indexing while achieving efficient logarithmic-time retrieval. Furthermore, RAG4GFM implements adaptive retrieval strategies for node, edge, and graph-level tasks, ensuring effective knowledge integration through graph fusion enhancement module. Extensive experiments conducted across diverse GFM applications demonstrate that RAG4GFM significantly enhances both the efficiency of knowledge updating and reasoning faithfulness. Our work establishes a systematic foundation for integrating RAG mechanisms into GFMs, thereby advancing the development of scalable and trustworthy GFM systems.