Title: RAG4GFM: Bridging Knowledge Gaps in Graph Foundation Models through Graph Retrieval Augmented Generation
Venue: NeurIPS 2025
Accepted: yes
Keywords: Graph Foundation Model

Retrieval-Augmented Generation

Graph Index

Graph Repersentation Learning
Abstract: 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. The task-aware retriever implements adaptive retrieval strategies for node, edge, and graph-level tasks to surface structurally and semantically relevant evidence. 
The graph fusion enhancement module fuses retrieved graph features with query features and augments the topology with sparse adjacency links that preserve structural and semantic proximity, yielding a fused graph for GFM inference.
Extensive experiments conducted across diverse GFM applications demonstrate that RAG4GFM significantly enhances both the efficiency of knowledge updating and reasoning faithfulness\footnote{Code: \url{https://github.com/Matrixmax/RAG4GFM}.}.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a RAG framework tailored for Graph Foundation Models (GFMs). The authors design a hierarchical graph indexing system that captures semantic and structural information at node, edge, and graph levels. A task-aware retrieval strategy is introduced to retrieve relevant subgraphs based on the nature of the query. Retrieved information is then integrated through a graph fusion enhancement mechanism to support downstream graph reasoning tasks. Extensive experiments are conducted across various GFM architectures and graph learning tasks, demonstrating the effectiveness of the proposed RAG4GFM framework compared to non-RAG-enhanced GFMs.\n\nSTRENGTHS AND WEAKNESSES:\n### Strengths\n* The paper is clearly structured and generally easy to follow.\n* The investigated research problem is interesting and important to the graph learning community.\n* The proposed framework is evaluated on a broad range of tasks (node classification, link prediction, etc.) and datasets, which improves validation.\n\n### Weaknesses\n- The core architectural design largely adapts existing concepts from RAG in LLMs to GFMs. The hierarchical indexing and fusion strategies are also based on known graph learning practices. While the engineering is careful, the novelty is incremental.\n- The **Graph Fusion Enhancement** module (Section 3.3) is insufficiently explained. The mathematical formulation (especially Equation 9 and the attention fusion mechanism) lacks intuitive explanation and alignment with the schematic in Figure 2(c).\n- The **retrieval sources used in experiments** (e.g., Code Graph, Wikipedia, GraphCite) are mentioned in diagrams but not clearly described or validated. It remains unclear whether they were truly used and how they relate to specific tasks.\n- The **ablation study** is limited. There is no breakdown analysis of the contributions of each component (e.g., different levels of indexing, individual fusion modules), which weakens the interpretability of the performance gains.\n\nQUESTIONS:\n1.  Please explicitly specify the external retrieval sources used for each task and dataset. While Figure 1 mentions Code Graph, Cite Network, and Wikipedia, it is not clear whether these were actually used in the experiments. Provide statistics on the source size, number of indexed subgraphs, and how these sources were constructed and queried.\n\n2.  Present qualitative examples of retrieved subgraphs and how they influence predictions. This would help demonstrate the framework’s interpretability and provide evidence of effective knowledge grounding.\n\n3. Consider evaluating the contribution of each index level (node, edge, graph) and each component of the Graph Fusion Enhancement module. This would help justify the design choices and confirm the necessity of all components.\n\n4. Section 3.3 is difficult to follow. Provide a clearer explanation of the attention-based fusion and structural enhancement, preferably with a simplified diagram or concrete example that aligns with Equations (4)–(9).\n\n5. The provided code is incomplete, which lacks direct support from GFM directory or instruction.\n\nLIMITATIONS:\nThe authors acknowledge limitations in the conclusion, but the discussion is quite general and lacks depth. A more concrete discussion of technical limitations and future mitigation strategies would enhance the value of the work.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces RAG4GFM, a Retrieval-Augmented Generation (RAG) paradigm tailored for Graph Foundation Models (GFMs), to address the challenges of knowledge updating and reasoning faithfulness. RAG4GFM consists of multi-level graph indexing, task-aware retrieval, and graph fusion enhancement modules. Experiments conducted on seven mainstream GFMs across six different graph learning tasks demonstrate that the proposed framework can effectively enhance the performance of GFMs.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The experimental results demonstrate that RAG4GFM effectively enhances the performance of various GFMs across multiple tasks, including node classification and link prediction.\n2. The proposed framework features a plug-and-play capability, making it simple and effective without requiring modifications to the GFM parameters.\n\nWeaknesses:\n1. The paper mentions the use of an LLM as a Retrieval Strategy Selector to decide on the features for retrieval based on the classification result. As a critical decision-making module, the paper lacks a discussion on the selector's own classification performance and the impact of its classification errors on subsequent reasoning.\n2. A core motivation of the paper is to solve the high cost of GFM knowledge updating. The introduction, in particular, notes that RAG can avoid the overhead of parameter updates. Although the authors provide a complexity analysis, the experimental section lacks direct experimental results on the efficiency improvements. For example, a comparison of the time and computational resource consumption between RAG4GFM and PEFT methods (e.g., GraphLoRA) to achieve a similar performance level on the same task is absent.\n3. Although RAG4GFM filters some information with a threshold, there is still a risk of introducing a significant number of noisy edges or damaging the topological integrity of the original graph when the retrieved graphs are relevant but of low quality. The authors lack an in-depth analysis of the denoising effect of this mechanism.\n\nQUESTIONS:\n1. In structural feature encoding, the authors concatenate LAPPE with the in-degree and out-degree of the nodes. Considering that LAPPE encodes global positional information while degrees represent local information, have the authors conducted experiments comparing the effects of using only LAPPE or other combinations? Is there a specific theoretical motivation for concatenating these three features?\n\n2. In the graph fusion section, the authors introduce a feature fusion threshold γ and an edge addition threshold δ, with both defaulted to 0.5. It is recommended to add a sensitivity analysis for these two parameters in the appendix to demonstrate the framework's robustness.\n\n3. In the ablation study, the accuracy of AnyGraph+RAG4GFM on Computers (Node Class.) is shown as 88.5 in the figure 3, but in Table 2, the value for the same setting is 79.56. Is this inconsistency a result of using different settings?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces RAG4GFM, a novel framework that integrates Retrieval-Augmented Generation (RAG) with Graph Foundation Models (GFMs) to address challenges in knowledge updating and reasoning faithfulness. RAG4GFM features a multi-level graph indexing architecture for efficient logarithmic-time retrieval, adaptive retrieval strategies for diverse node, edge, and graph-level tasks, Then, a graph fusion enhancement module to integrate retrieved subgraphs with user queries. The framework dynamically augments GFMs with external graph knowledge, improving reasoning fidelity and reducing hallucinations.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths \n\n1.RAG4GFM is a comprehensive RAG paradigm designed explicitly for graph data and GFMs, offering a foundation for integrating RAG mechanisms into GFMs.\n\n2.The framework significantly enhances the efficiency of knowledge updating in GFMs by avoiding parameter updates through dynamic external knowledge retrieval.\n\n3.By grounding GFM predictions in external graph data, RAG4GFM markedly improves reasoning faithfulness, reducing the risk of hallucinations.\n\n\n Weakness\n\n1.The paper does not report error bars or confidence intervals for the experimental results, making it difficult to assess the statistical significance of the findings.\n\n2.RAG4GFM's performance is closely tied to the performance of the underlying GFM and LLM, potentially inheriting their limitations or propagating biases.\n\n3.The paper could provide more rigorous theoretical analysis to support the effectiveness of the proposed framework, particularly regarding the integration of retrieved knowledge and the impact on reasoning faithfulness.\n\n4.Typos and Grammar Issues:\n•\t\"GFMPost-Molecular\" should be \"GFM Post-Molecular\"\n\n•\t\"Retriver\" should be \"Retriever\"\n\n•\t\"Userquery\" should be \"User Query\"\n\n•\t\"Please classify Multi-Level Graph Index Retriver Enhancement is a cite this graph\" contains several errors and should be rephrased for clarity.\n\nQUESTIONS:\nPlease address my concernslisted above.\n\nLIMITATIONS:\nYES", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe article introduces the RAG4GFM framework, which addresses the challenges of knowledge updating and inference fidelity in graph based models (GFMs). By integrating multi-level graph indexing, task aware retrieval, and graph fusion enhancement mechanisms, it achieves efficient external graph knowledge retrieval and integration. The framework has demonstrated significant performance improvements in various graph learning tasks such as node classification, link prediction, and graph classification, and its effectiveness and superiority have been verified through extensive experiments.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths \n\n1. The RAG4GFM framework is the first to systematically apply retrieval enhanced generation techniques to graph based models (GFMs), providing a new approach to addressing the challenges of knowledge updating and inference fidelity in graph learning models. This innovation not only enriches the theoretical system of graph representation learning, but also opens up new possibilities for the practical application of graph learning models, and has high academic value.\n\n2. The paper comprehensively verifies the effectiveness and superiority of the RAG4GFM framework through a large number of rigorously designed experiments. The experiment used multiple graph datasets from different domains and compared various baseline methods, including the latest retrieval enhancement method and graph out of distribution generalization method, fully demonstrating the performance improvement of the RAG4GFM framework in various graph learning tasks such as node classification, link prediction, and graph classification. In addition, the ablation experiment further validated the contributions of each component in the framework, enhancing the credibility of the experimental results.\n\n3. The logical structure of the paper is very clear and the hierarchy is distinct. From the explanation of research background and motivation, to the detailed design and implementation of the RAG4GFM framework, to experimental verification and future work prospects, each part is closely connected and rigorously discussed, which helps readers gradually deepen their understanding of the principles and applications of the RAG4GFM framework.\n\nWeaknesses\n\n1. Although the HNSW algorithm mentioned in the paper has provided efficient retrieval performance, there is still room for further optimization of indexing efficiency for certain specific application scenarios.\n\n2. The current RAG4GFM framework mainly focuses on the retrieval and fusion of graph data. In the future, it may be considered to integrate information from other modalities such as text and images into the framework, thereby enhancing the model's cross modal reasoning ability and enabling it to handle more complex and multi-source data.\n\n3. For application scenarios that require real-time response, such as online recommendation systems or real-time graph analysis, this paper can further investigate how to reduce computational delays in retrieval and fusion processes, improve the real-time performance of the model, and meet the needs of practical applications.\n\nQUESTIONS:\n1. In the process of constructing multi-level graph indexing, although a hybrid feature encoding mechanism combining text features, node embedding, edge representation, and graph level encoding has been proposed, how is the weight or importance of each feature determined specifically? Do we need to adjust the weights of these features in different types of graph data to achieve better retrieval performance?\n\n2. How to ensure that the LLM based task type classifier used in the task aware retrieval mechanism can accurately classify various complex and diverse user queries? In practical applications, how will this mechanism handle task queries that cannot be clearly classified?\n\n3. Can the RAG4GFM framework efficiently support online retrieval and fusion for dynamic updating scenarios of graph data, such as graph structures or node features that constantly change over time? Are there any corresponding experiments or cases to demonstrate its performance in such dynamic scenarios?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-30"}