RAG4GFM: Bridging Knowledge Gaps in Graph Foundation Models through Graph Retrieval Augmented Generation
Authors: Xingliang Wang
Zemin Liu
Junxiao Han
Shuiguang Deng
Venue: NeurIPS 2025 oral ()
Keywords: Graph Foundation Model
Retrieval-Augmented Generation
Graph Index
Graph Repersentation Learning
Primary area: deep_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}.}.

RAG4GFM: Bridging Knowledge Gaps in Graph Foundation Models through Graph Retrieval Augmented Generation
Xingliang Wang'
Zemin Liu Junxiao Han Shuiguang Deng'
"Colleee of Computer Science and Technology, Zhejiang University, Hangzhou, China School of Computer and Computing Science, Hangzhou City University, Hangzhou, China (wangxingliang liu. zemin, dengsg) @zju. edu. cn, hanjx@hzcu. edu. cn
Abstract
Graph Foundation Models (GFMs) have demonstrated remarkable potential across graph learning tasks but face significant challenges knowledge updating and rea- soning faithfulness. To address these issues, we introduce the Retrieval-Augmente Generation (RAG) paradigm for GFMs, which leverages graph knowledge retrieval. end-to-end framework that seamlessly integrates multi-level graph indexing. task-aware retrieval, and graph fusion enhancement. RAG4GFM implements 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 fea- tures and augments the topology with sparse adjacency links that preserve structural and semantic proximity, yielding a fused graph for GFM inference. Extensive ex- periments conducted across diverse GFM applications demonstrate that RAG4GFM significantly enhances both the efficiency of knowledge updating and reasoning faithfulnes
We propose RAG4GFM
1 Introduction
has achieved remarkable progress across diverse graph tasks, such as node classification and link prediction. Concurrently. the substantial success of Large Language Models (LLMs) 4 5) has revolutionized natural language processing (NLP) and motivated the development of Graph Foundation Models (GFMs) 3 77. GFMs adapt large-scale pre-training techniques to graph data, enabling powerful cross-domain generalization and multi- task adaptability for diverse graph- based applications. However, two practical challenges remain prominent: knowledge updating 8 9 iQj.e., keeping models current as graphs evolveand faithful reasoning
Graph representation learning 11 2 3
i... generating accurate and factually consistent outputs.
On the hand, graph data evolves rapidly, GFMs usually require knowledge updates within hours or a day 15). massive parameter scales impose substantial computational demands [6] 17]. On the other hand, handling complex graph structures (18) and coupling GFMs with LLMs may induce hallucinations [19 20 21], e.g., generating fictitious features or nodes, leading to unfaithful or factually inconsistent outputs.
Some recent studies have attempted to address these challenges. Parameter-efficient fine- tunint (PEFT) methods on GFMs, such as GraphLoRA 22 23 and G-Adapter (24), aim to reduce the cost
"Correponding authors.
"Code: https://githut http:p/:///tttta....a.aaaaaaaaaa
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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(b) Graph knowledge updating based on Traditional training
Cite Network
Code Graph
Wikipedia Graph
(c) Graph knowledge updating based on RAG paradigm
Pre Training
Predict
Fused Graph Knowledge
Task Specifie GFM Predict
Post- Training
Molecular
Graph
Tusk-Aware Retriever
Graph Fusion
This graph is a cite network
Please classify
Multi-Level Gruph Index
Enhancement
this graph
Text/Graph Encoder
This graph is an acyclic compound
(a) User query
Figure I: Comparison between conventional training-drive driven GFMs (b) and RAG4GFM (c). (a) A user query comprising a hatural-language description and a graph: (b) Conventional GFMs rely on task-specific post-training to integrate new knowledge, incurring substantial computational cost; (c) RAG4GFM integrates external graph knowledge at inference time via retrieval and fusion, ensuring faithful reasoning without additional post-training
of knowledge updating by adapting pre-trained models via task-specific post-training (as depicted in Figure [b)). However, these methods still require substantial computational resources and remain vulnerable to catastrophic forgetting. Concurrent efforts to improve reasoning faithfulness focus on enhancing the quality of training data [25] (26 or refining GFM architectures [27 (28.. Despite these ad advnncmmnns, reliable reasoning remains challenging because these methods largely operate within fixed parameters and training data, lacking mechanisms to dynamically ground predictions in
verifiable, task-relevant graph evidence.
Retrieval- -aggeettdd generation (RAG) [2] 30 offers an appealing alternative: by retrieving external evidence at inference time, RAG circumvents frequent parameter updates, adapts to evolving corpora, and can mitigate hallucinations (31 32) However, extending this RAG paradigm to graph data and GFMs raises three challenges: (1) Indexing: how to build graph indices that preserve structure and
serve both RAG and GFMs? (2) Retrieval: how design retrieval mechanisms that accommodate task heterogeneity? (3) Augmentation: how to augment the dask-specific query with retrieved graph evidence?
To overcome these challenges, we propose RAG4GFM, a unified RAG framework designed explicitly for graph data, graph tasks, and GFMs. To knowledge, it is among the first comprehensive designs that operationalize RAG for GFMs. As illustrated in Fig. (c). RAG4GFM augments a GFM with external graph knowledge through multi-stage pipeline. Firstly, the "multi-level graph index" module processes raw graph data into an efficient, structure-aware index. Secondly, the "task-aware retriever" module identifies the user intent and retrieves relevant candidate subgraphs from the constructed index. Finally, the "graph fusion enhancement" module integrates retrieved subgraph knowledge into the user query, enriching its features and structure. By grounding GFM's predictions in retrieved graph evidence-rather than solely in its internal parametersRAG4GFM flexibly adapts to evolving knowledge and markedly enhances reasoning fidelity.
Specifically, we first establish an efficient and flexible multi-level graph indexing module. This module designed to preserve graph semanties and structural topology by integrating four complementary indices: text features extracted via LM encoders, node embeddings that capture structural information via Laplacian positional encoding 33 edge-level representations, and graph-level embeddings. By leveraging the hierarchical structure index, the module achieves comprehensive semantic-structural encoding with logarithmic-ting complexity.
Second, building on this index, we propose a task-aware retriever module that adapts to diverse downstream task types. This module dynamically selects appropriate indices and retrieval strategies based on the task typology (node, edge, or graph), retrieving relevant textual and structural features for node tasks, lege-level representations for edge tasks, and graph-level embeddings for graph tasks. Additionally, we use a fusion reranker to consolidate and prioritize retrieval results from both graph features and semantic spaces.
Finally, we introduce a graph fusion enhancement module that integrates retrieved evidence with the query graph effectively. This module consists of two components: a feature-fusion component

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that integrates the retrieved graph features with query features based on attention weights calculated from similarity scores, while a topological-structure enhancement component augments the query graph's s connectivity by combining adjacency information from relevant retrieved graphs through sparse matrix operations. This structured fusion approach surpasses sequential concatenation in traditional RAG by better aligning with graph connectivity, preserving topological information, and accommodating multimodal user queries within the GFM context.
The GFM then performs inference on this fused graph, allowing it to leverage the augmented context for more accurate predictions and a significant reduction hallucinations. Extensive experiments across multiple GFM applications demonstrate RAG4GFM 's superiority in efficiency and reliability, with safeguards intended minimize the risk of pre-training data contamination.
2
Related Work
Graph Foundation Models. GFMs leverage large-scale pre-training for versatile knowledge transfer,
exhibiting reasoning and domain adaptation capabilities [34 They encompass: (1) Self-supervised
learning approaches, such as masked auto-encoding (e.g., GraphMAE (27)); (2) Graph-language
model alignment methods that facilitate multimodal pre-training, such as GraphGPT [16]; and (3)
Recent architectural innovations aimed at enhancing transferability via Mixture of Experts (MoE), such as AnyGraph [17,, structural understanding through topology-aware tokenization as in Open- Graph (35, or improving generalization through property-driven training, such as GraphProp (28).
However, GFMs are constrained by inefficient knowledge updating, typically requiring extensive retraining [16], and difficulties in ensuring reasoning reliability over complex graph structures, potentially leading to biases [[]]
Graph Indexing and Retrieval. Graph indexing and retrieval have progressed from general vector methods to structure- techniques. Initial advancements focused optimizing vector-space retrieval efficiency, including adaptive algorithms, such as FLANN (36), and hierarchical graph structures for efficient search, such HNSW [37 [3]. These formed the basis for scalable libraries such as FAISS (39]. While traditional Information Retrieval (IR) methods like BM25 44 exist, vector-based strategies are more directly applicable graph data retrieval. Despite these advance- ments, developing retrieval systems that effectively integrate multimodal data (e.g., text with graph structures) and generalize across diverse graph-specific tasks remains a significant challenge.
Graph Retrieval-Augmenntd Generation. RAG [30] enhances LLMs by integrating external knowl- edge through a "retrieve-generaas pipeline. Its progression includes: (1) Foundational end-to-end trainable RAG frameworks for knowledge-intensive NLP 30; (2) Architectural refinements, includ- ing multi-hop retrieval for comprehensive knowledge gathering and integrated retriever-gence gereerttorrrr optimization 4 42 and (3) Recent graph-centric extensions, featuring methods that empha- size structure-awars retrieval, such as GraphRAG [43, computational optimization, such Ligh- IRAG [44), or hybrid knowledge integration, such as HybridRAG [45). Nevertheless, applying RAG to graph data still encounters critical challenges. Standard vector retrieval techniques often fail to adequately represent complex graph topology [46). Morcover, the creation of specialized graph indexing and retrieval mechanisms tailored for the unique requirements of graph RAG remains an active research direction.
3 RAG4GFM
RAG4GFM is a RAG framework tailored for graph data and GFMs, integrating external graph knowledge into the GFM inference process. The pipeline initiates with multi-level graph indexing (Figure(ja)) where RAG4GFM constructs HNSW-based (77 multimodal indices for graph corpora. Next, the task-aware retrieval mechanism (Figure 2b) processes user queries (text and graph structure) and adaptively retrieves relevant subgraphs. Subsequently, the graph fusion enhancement module (Figure cc) integrates these retrieved subgraphs with the original query graph through a two-step process: iitention-based feature fusion and adjacency matrix- topological fusion. Finally, the fused graph is input to the base GFM for inference.
3

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Muiti-Level Graph Indexing
Molecular
Construct
Wikipedia Graph
Graph
Cite Network
Code Graph
Prainnginin
Please classify this graph
Fincode
Retrieve
Enhancement
Predict
This groph acyclic compound
query
Tware Retriever
Graph Fusion
Zolen
Enhancement
Zoom
Tesp Verker Simibrity
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Subggraphs
0.52
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Encoder
Graph Index
Multi-Level
based Renanker
Ranked Sungraphs Scoce
Attestin--eee besee---eeeeee Feature
Pusion Graph
(b) Task-Aware Retriever
(c) Graph Fusion Enhancement
Figure 2: The overall architecture of RAG4GFM RAG4GFM dynamically augments Base GFM by: (a) constructing a "Multi-Level Graph Index" (b) retrieving and reranking task-relevant subgraphs based the query via "Task-Aware Retriever"; and (c) fusing the retrieved subgraphs with query information through the "Graph Fusion Enhancement module", before generation.
3.1 Multi-level Graph Indexing
The effective application of the RAG paradigm in GFMs hinges on constructing graph data indexing systems that preserve graph completeness while ensuring efficient querying. Unlike traditional LLMs, user queries GFM application scenarios typically present a hybrid form of text and graph data. The complex topology and multimodal attributes of graph data pose significant challenge: designing an index system that simultaneously maintains graph structural fidelity and supports efficient retrieval. To address this challenge, propose multi-level graph indexing approach that first encodes mixed graph features, and then constructs hierarchical indices to enable scalable querying and retrieval.
Mixed Feature Encoding Mechanism. To comprehensively represent the multidimensional charac- teristics of graph data, this work designs four complementary feature encoding mechanisms: node feature encoding, structural feature encoding, edge feature encoding, and graph-level encoding, which align with the hierarchical reasoning capabilities discussed in Section This mixed encoding strat- egy realizes multi-granular representations of graph data, thereby accommodating diverse retrieval requirements under varied tasks.
Node Feature Encoding: For nodes in the graph, we employ pre-trained language models to seman- tically encode the textual descriptions of node v as (v) = LM(r). This method fully leverages the advantages of pre-trained language models in semantic understanding, mapping the textual information of nodes high-dimensional semantic spaces.
Structural Feature Encoding: The topological position of nodes in graph contains rich structural information. We combine Laplacian Positional Encoding (LAPPE) with node degree metrics to construct structure-aware feature representations as
(v) = LAPPE(v) @ (vg @ Degocr(v),
where LAPPE(U) is the positional encoding based on the eigendccomposition of the graph laplacian matrix, Deg (v) and Degour (v) represent the in-degree and out-degree of node v, respectively, and @ denotes feature concatenation. Laplacian positional encoding effectively captures the position information of nodes in the global graph structure. while node degree information reflects local connection patterns. This encoding method is invariant to node permutations and graph isomorphism, accurately capturing nodes' relative positions within the topology.
Edge Feature Encoding: To capture structural properties and topological roles of edges, we transform the original graph into its corresponding line graph. We then compute the LapPE for the nodes in this line graph. This approach allows us to generate a feature representation (,(u, v) for each edge in the original graph that effectively encodes its structural context within the overall

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graph topology. This approach effectively differentiates edges by their connectivity patterns and structural roles within the graph.
Graph-level Encoding: To obtain a holistic representation, we integrate node features, edge features, and graph statistical features:
(G) ANODE (G) REDGE (C) @ IISTATS (G), where hyODE (G) MEAN (w) 1 EV), hgnge (G) = MEAN(L,(u,,) (u, E), and HTTATS (G) [I]]EE),
(2)
where hNopE (G), HRDOE (G) and Iss (G) represent node feature aggregation, edge feature aggre- gation, and graph statistical features, respectively. p(G) denotes the graph density, i.e., the ratio between actual and maximal possible edge counts. This comprehensive representation method fully captures the overall characteristics of the graph, providing effective support for graph-level tasks.
Hierarchical Index Construction. With the mixed features obtained, we proceed organize them into an efficient multi-level index structure. This research selects the Hierarchical Navigable Small World (HNSW) as the theoretical foundation for the index structure. The hierarchical search pattern of HNSW aligns well with the multi-scale structure inherent in graph data. Accordingly, we construct a four-level hierarchical index structure covering node-, structure- edge- and graph-level representations: HINDEX = (h,(v). h, (v), he (u, v), hy (G)).
This multi-space index design offers primary advantages: firstly, it overcomes the limitations of single representation methods by comprehensively capturing both semantic and structural graph information while flexibly supporting diverse node, edge, and graph-level retrieval tasks through unified interface; secondly, it guarantees efficient O(log, N) retrieval time, crucial for large-scale graph data.
3.2 Task-aware Retrieval
GFMs span diverse application scenariossuch as node classification, link prediction, and graph classificationeach with distinct retrieval requirements. Traditional unified retrieval strategies fail to capture the heterogeneity of such tasks, often resulting in suboptimal efficiency and precision. To address this, we propose a task-aware retrieval framework that dynamically adapts retrieval strategies according to task characteristics, thereby improving both retrieval accuracy and computational efficiency.
Retrieval Strategy Selector. To accommodate heterogeneous graph tasks, the retrieval module must interpret user intent and select optimal retrieval strategies accordingly. We employ an LM-based task classifier that performs joint analysis of the natural language query and its associated graph context to predict the task type: T(g) E {NODE, EDGE, GRAPH), where denotes the LLM -iLM-immmmmeeted task discrimination function, and represents the user's natural language query. The predicted task type determines which feature spaces and retrieval operators are subsequently activated.
Hybrid Feature Retrieval.
Based on task classification results, the retrieval module adaptively selects task-relevant feature spaces to minimize irrelevant noise and align with downstream GFM objectives.
For node-level tasks, such node classification and node regression, jointly query both node and structural feature spaces to capture fine-grained local semantics. For edge-level tasks, including link prediction and edge classification, we leverage node and edge features to represent pairwise relational semantics. For graph-level tasks, such as graph classification and regression, we utilize holistic graph embeddings augmented with aggregated node features.
To obtain the final retrieval score for query q under the graph context C, we apply a reciprocal rank fusion (RRF) strategy to aggregate results from multiple retrieval channels:
S(glh,,
Σ CEF(h,,C) d (C)k
where F(h,, C) denotes the retrieved feature set, is a smoothing constant, and rank represents the rank of candidate among the top-k results. The corresponding feature sets for different tasks
5

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are defined as:
(hr(v), h,
if = (node-level); if (u, v) (edge-level);
F(b, C) (hi(v), h,)
(h,(u), (y),), (u,
(4)
if G (graph-level).
Unlike conventional methods that rely solely on node-level feature aggregation, our hybrid retrieval framework integrates semantic and structural eues across multiple levels, enabling task-adaptive retrieval that improves both precision and efficiencykey challenges emphasized in the introduction.
Unlike traditional methods that rely solely node feature aggregation, approach constructs more comprehensive graph representations through the integration of multi-level information.
3.3 Graph Fusion Enhancement
In conventional RAG frameworks for NLP tasks, retrieved text fragments are typically fused via simple concatenation or mean pooling of embeddings. However, when applied to graph data, such approaches fail to preserve topological relationships and path dependencies, resulting in substantial loss of structural information. Moreover, node importance in graphs depends heavily on positional and connectivity patterns, which traditional fusion methods cannot effectively capture. To address these limitationsparticularly the diructure-semanti imbalance highlighted in Section [] we propose dual graph fusion enhancement mechanism, consisting of (1) attention-basee feature fusion and (2) topological structure enhancement.
Atention Feature Fusion. We introduce similarity- -ased dynamic weighting mechanism that adaptively determines the importance of each retrieved graph according its semantic similarity with the query graph. This mechanism enables the model to assign higher weights o to more relevant retrieved graphs, thereby focusing knowledge most pertinent the query.
h" N
h.
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(edge-level, destination)
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h" G
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8 = SOFTMAX(h, (hg + hb))),
(graph-level, attention)
E where h is the enhanced target node feature, h, is the original node feature, sRC and E the enhanced source and destination edge features, h is the enhanced target graph feature, Vi is the node set of the i-th retrieved graph, and y is the feature fusion threshold (default value 0.5) used to filter low-relevance retrieved results. h and h represent the global features of the query graph and the i-th retrieved graph, respectively, and & the number of effective retrieved results.
DST are
Compared to traditional feature fusion methods, the attention feature fusion mechanism proposed in this research adaptively emphasizes highly relevant knowledge through dynamic weight allocation, effectively reducing noise impact while utilizing threshold filtering mechanisms to avoid interference from low-quality retrieved results.
Topological Structure Enhancement. Complementary to feature fusion, the topological structure enhancement module focuses on enriching the query graph's 's connectivity patterns. It selectively incorporates structurally relevant edges from retrieved graphs, weighted by their graph-level relevance scores of thereby augmenting the query graph with semantically aligned structural information. Implemented via sparse adjacency operations, this module efficiently handles large-scale graph data while maintaining topological consistency.

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Given the adjacency matrix A of the query graph and the set of adjacency matrices A1, A2. A2. Ae...... of retrieved graphs, the enhanced adjacency matrix is computed as:
As
A' + a
A. -aa > 7),
(10)
where a? serves as the relevance weight. To suppress noisy edges, we apply threshold filtering such that A / > 6), where is the edge addition threshold (default value 0.5). This sparse matrix-based implementation achieves efficient large-scale processing while preserving graph sparsity and interpretability.
Other analyses. Due to space limitations, present analyses the complexity in Appendix
4 Experiments
4.1 Experimental Setup
Datasets. To evaluate the effectiveness and generality of RAG4GFM we conduct experiments on diverse collection of graph datasets covering six task types. For node classification and link prediction, we adopt the latest TAG benchmark [48] prevent potential data leakage from GFM pre-training. For other tasks, we select datasets that, to the best of our knowledge, were not used in any GFM pre-training phase, verified through public model documentation and release notes. Table summarizes the dataset statistics, with additional details in Appendix B.1
Table I: Statistics of the dataset used in our experiments. "Task" denotes the downstream task type: NC (Node Classification). NR (Node Regression), LC (Link Classificationn, LP (Link Prediction), GC (Graph Classification), and GR (Graph Regression). "Graphs" indicates the number of graph instances; a value of denotes a single large graph.
Dataset
Nodes
Edges
Graphs
Domain
Task
48
76,875
1,554,578
E-commerce
NC,LP
Books-History
48
41,551
358,574
1
E-commerce
NC, LP
Ele-Computers
87,229
721,081
E-commerce
NC,
Ele-Photo
48
48,362
500,928
E-commerce
NC, LP
MiniGCDataset
49
21,909
177,875
1,000
Synthetic
GC
BAZMotilDataset
[00
25,000
51,392
1,000
Synthetic
GC
Chameleon
2.277
36,
Wikipedia
NR
WN18Datasct
522
40,943
151,442
Knowledge
LC
QM7bDataset
53
108,165
1,766,695
7,211
Biology
GR
Baselines. We evaluate RAG4GFM against state-of-the-art approaches from three categories:
(I) Prompt Engincering Methods: Few-shot Learning [54). Chain-of-Thought (COT) 155] IR- augmented COT [56]. These methods focus on structuring the input prompt to guide the GFM's inference without altering model parameters, leveraging strategies like few-shot examples plicit reasoning steps. (2) Retrieval-
Methods: VanillaRAG [30]. GraphRAG [43), G-Retriever [57]. These methods augment GFMs with external knowledge retrieval and ground predictions relevant evidence, ranging from text to graph-aware retrieval. (3) Graph Out-of-
Distribution Generalization Methods: Prototype (58, GNNSafe [59]. These approaches focus
the ability to generalize to unseen domains, structures, or distributions, emphasizing robustness and transferability.
GFMs. We evaluate RAG4GFM on seven representative GFMs, grouped by their predictive ar- chitecture into three types: (1) GNNs as predictor: OpenGraph (35] and AnyGraph [17]. (2) Co-learning GNNs and LLMs: GLEM 660. (3) LLMs as predictor: GraphGPT [66. HiGPT [l]. LLaGA 662, and GraphAdapter 63 Further details and descriptions of these models are provided
in Appendix B.2
4.2 Effectiveness of RAG-Enhanced GFMs
Our first research question investigates whether RAG-based mechanisms enhance the performance of existing GFMs across various graph tasks. Table 2 shows the results for node classification and
7

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link prediction, while additional findings for node regression, link classification, graph classification, and graph regression are reported in Appendix C.I Note that some GFMs lack results for tasks not supported by their architectures; for instance, OpenGraph does not support graph-level classification.
Table 2: Results of Node Classification and Link Prediction on Four Datasets
Datasess
Model
Computers
History
Fimess
Photo
ROC-AUC
Recall
Ace
ROC-AUC
Recall
Acc
ROC-AUC
Acc
ROC-ALC
Recall
Node Classification Results
HIGHT
76.82
61.45
58.76
60.45
59.78
54.34
70.12
58.67
57.23
63.88
57.45
53.21
HIGFT-RAGIGEM
79.34
66,89
66.12
63.21
63.45
59.76
72.89
63.78
64.32
67.50
63.24
60.45
GraphGPT
75.45
58.23
59.88
69.34
59.23
52.90
GraphGPT- RRAGGTT-RAGGMMM
81.23
67.56
67.45
64.92
60.54
74.88
64.56
69.45
61.23
GLEM
79:45
63.56
60.12
54.67
$7.23
52.45
73,89
60:45
58.90
61.23
56,23
53.45
GLEM+ RAG4GFM
83.12
68.90
68.23
59.89
61.78
57.34
75.45
65.12
66.78
66.90
62.56
59.34
LLaGA
71.23
59.45
55.67
58.90
58.23
53.21
64.56
55.34
54.45
59,78
55.45
51.78
LLaGA+RAG4GFM
76.89
63.45
61.23
62.34
58.56
71.90
61.23
58.34
GraphAdapter
76.78
62.78
59.34
52.34
56.45
51.23
69.90
59.78
57.45
65.78
58.45
54.67
81.23
67.45
67.23
$7.89
61.23
56.78
73:45
64.34
64.56
69.90
64.23
61.56
OpenGragh
74.67
61.32
57.73
62.71
61.58
55.82
67.59
57.80
56.74
60.93
56.97
52,48
OpenSIngHHRACCGFMM
79.38
65.01
63.92
73.11
62.77
62.70
59.10
AnyGrap
73.15
60.89
57.23
61.89
60.23
$1,90
67.90
57.45
55.89
61.23
56.45
52.67
79.56
66.12
65.34
63:45
64.12
60,23
72.34
62. 62.89
63.12
67.89
62.78
59.45
Link Prediction Resalts
OpenGraph
35.62
Ope*GiiggHHAAHHGFM
58.21
45.18
43.66
42:27
44.14
39.92
52.00
40.71
42.25
45.82
41.77
37.76
HIGH
55.25
40.68
37:43
38.99
38.56
33.13
49.44
36.61
35.90
42.06
36.26
32.05
RIGPT-RAGAGIM
58.00
45.85
45.28
41.94
43.02
37.76
52.29
41.78
43.18
45,93
42.52
39.17
GraphGPPT
54.27
41.03
37.16
37.71
38.04
32.09
48.51
36.72
35.06
40.88
35.59
31.13
GrihhGPT- RAG4GFM
46.05
45.93
42.96
43.62
39.04
53.14
42.68
44.27
47.74
39.81
AnyCraph
52.17
38.53
36.17
10.01
39.68
33.07
16.
35.47
34.30
39.52
35.29
31.06
58.26
44.92
43.94
41.74
43.26
38.84
$1.34
40.59
41.83
45.97
41.53
38.12
Across all four datasets, the RAG4GFM yields consistent and substantial improvements, demonstrat- ing its strong generalization capability. For instance, GLEM+RAG4GFN, achieves 83.12% accuracy on node classification in the Computers dataset, representing a relative gain of approximately 5.5% over GLEM. Similarly, GraphGPT+RAG4GG forming its vanilla counterpart by 5.1%.
attains 60.37% accuracy on link prediction, outper-
The improvements are consistently observed across all four dotasetsComputers, History, Fitness, and Photohighlighting the broad applicability of our approach. For example, GLEM+RAG44FM achieves 83. 83.12% accuracy node classification in the Computers dataset, a relative gain of 5.5% over the baseline GLEM. Similarly, GraphGPT+ AG4GGFM attains 60.37% accuracy on link prediction, outperforming its vanilla counterpart by 5.1%. These consistent trends indicate that RAG-based augmentation benefits both GNN-based and LLM-based GFMs. To elucidate the source of these improvements, we analyze the core design of RAG4GFM The Multi-level Indexing, coupled with the Task-Aware Retriever module, ensures that each GFM retrieves the most relevant external subgraphs and feature spaces efficiently, reducing noise and retrieval latency. Finally, the Graph Fusion Enhancement module integrates retrieved information into the query graph through both feature-level and topology-leve fusion. This integration refines node embeddings for classification, strengthens edge inference for link prediction, and provides more coherent context for graph-level reasoning. These components operate synergiticalllyenoddii richer local context, retrieving task-relevant external knowledge, and integrating it in a strueture-consistent manner.
Overall, the results demonstrate that RAG4GFM effectively overcomes the static- parameer limitation of conventional GFMs by dynamically grounding predictions in external structured knowledge. This design directly addresses the integration bottleneck highlighted in the introduction, leading to more accurate, contextually grounded, and robust graph-level reasoning across tasks without requiring additional post-training.
4.3 Ablation Study on RAG4GFM
To quantitatively evaluate the individual contributions of RAG4GFM 's key components, we perform a series of ablation experiments using AnyGraph as the base GFM under identical hyperparameter settings for fair comparison. We analyze both architectural modules (retrieval, fusion, indexing)
8

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and feature-level encodings to understand how each design choice affects performance on node classification (Computers) and link prediction (History) tasks.
Variants on Core Architecture. w/o RAG. Removes the entire retrieval-augmenned generation (RAG) pipeline; the GFM operates without external knowledge retrieval or integration. w/o GF. Retains retrieval but replaces the specialized graph fusion module with a naive concatenation strategy, removing the semantic-structural alignment mechanism. w/o GI. Replaces the hierarchical graph indexing with basic text-similarity index, assessing the importance of structure-awan retrieval.
Variants on Feature Encoding. To further assess the effectiveness of our structural feature design, we compare three encoding strategies: LAPPE only. Uses Laplacian positional encodings, capturing global structural information but limited in local awareness. Node Degree only. Uses node in/out- degree as simple local centrality features. LAPPE Degree. Combines both global positional and local topological cues, aligning with the principle of global-local structural complementarity.
Computers (Node Classrication)
History (Link Prediction)
Performance (%6
70
011
65-
45.4
41
60.
66.3
21A
C6
15
20
R654CPM
wa R4G
wil
LAMPE only
Node Regres only
Degiet
BAGKSPM
LAPPE only
Node Deginen only
LAPEE Degrte
Figure Ablation study results for RAG4GFM on node classification (Computers) and link prediction
(History) tasks. Higher values are better.
As illustrated in Figure 3 the full RAG4GFM configuration consistently achieves the best results across both tasks. Among the architectural variants, w/o RAG exhibits the largest drop, confirming that retrieval is crucial for integrating dynamic external knowledge. Removing the graph fusion module (w/o GF) also decreases accuracy and AUC, showing that our fusion mechanism is essential for coherent reasoning beyond naive feature concatenation. The performance reduction of w/o Gl highlights the role of hierarchical, structure-aware indexing in precise, low-noise retrieval.
Regarding feature encoding. models using only LAPPE or only node degree perform noticeably worse than the joint configuration. The combined LeprE+Degree variant recovers nearly the same performance as the full model, validating that global- structural complementarity enhances graph representation learning. This observation is consistent across both Computers and History tasks, underscoring that balanced structural cues benefit retrieval and fusion.
All componentsRAC retrieval, graph fusion, hierarchical indexing, and global-local feature encod- ingcontribute jointly RAG4GFM 's effectiveness. Their synergy enables efficient updates, faithful reasoning, and more accurate predictions across diverse graph tasks.
4.4 Comparison with Other Knowledge Updating Methods
We evaluate tasks where external knowledge is critical and frequently updated, such knowledge- intensive node classification.
Results in Table firming its capability retrieve and integrate relevant external information for accurate prediction. Retrieval-enhanced (RE) methods generally outperform woonpt-nngineering (PE) and graph out-of- distribution (G-OOD) methods. Among RE baselines, graph-aware models such as GraphRAG and G-Retriever achieve stronger results than lexical retrievers, yet RAG4GFM delivers further gains by explicitly fusing semantic and structural representations. PE methods provide only marginal improvements, while text-only augmentation (e.g.. IR-augmented CoT) remains insufficient for complex graph reasoning. G-OOD-oriented approaches (Prototype, GNNSafe) underperform in our setting, they emphasize distributional robustness rather than dynamic knowledge updating.
3show show that RAG4GFM consistently outperforms all competing approaches, con-

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Table 3: Performance comparison of RAG4GEM with knowledge updating baselines on node classfiiation(Compueee link prediction (History), and graph classification (MiniGCDataset).
Computers (Node Class.)
History (Link Pred.)
MiniGGDutaset (Graph Class.
Category
Method
Accuracy
ROC-AUC
Accuracy
ROC-ALIC
Accuracy
ROC-AUC
Few-shot I Larnning
X4
09.62
59.88
$8.27
56.35
59.24
46.26
Prompt Engineering
62.46
59.28
IR IR-augmented COT
56
69.68
63.62
58.58
59.46
59.41
48.59
Graph Distribution
Prototype
71.04
60:77
60.15
56.86
60.05
47.48
GNNSafe
70.89
63.56
59.62
61.44
60.57
49.42
VanillaRAG
MI
71.58
64.63
61.42
60.57
63.46
51.58
Retrieval-Enhameed Enhunced
Retrieval-
GapERRGG
74.47
64.61
59.84
63.18
G-Retriever
75.06
64.95
60.27
61.97
63.73
52.32
RAG4GFM (Ours)
AnyCrrppe-buse
79.56
66.12
63.45
64.12
65.51
51.88
framework. Compared with traditional fine-tuning or static knowledge-graph integration, the RAG paradigm offers flexible and efficient mechanism for updating GFMs with external knowledge. It achieves this without costly retraining while maintaining reasoning fidelity.
In summary, RAG4GFM demonstrates clear advantages
Beyond predictive performance, further examine the efficiency of RAG4GFM compared to GraphLoRA [22] a representative parameter-efficiet fine-tuning (PEFT) approach. reports the time and GPU memory required to reach identical accuracy targets. RAG4GFM achieves comparable accuracy while reducing runtime by up to 7.0 and GPU memory usage by over %0%. This efficiency arises from its retrieval-basee based updating paradigm, which avoids gradient-basee optimization and large parameter storage. Morcover, since RAG4GFM refreshes knowledge through lightweight retrieval and fusion rather than re-training, scales favorably to frequent graph updates and essurce-constrainee environments.
Table 4: Efficiency and memory comparison between RAG4GFM and GraphLoRA 22 under
identical accuracy targets. Reported metries include training time and peak GPU memory usage.
GFM
Dataset
Target Ace
Time
Peak GPU Memory
GraphLoRA + AnyGraph
Computers
78%
7.32 Hours
25.23 GB
RAG4GFM + AnyGraph
Computers
78%
63 Minutes
9.86 GB
GraphLoRA + HiGPT
History
63%
5.87 Hours
17.18
RAG4GEM + HiGPT
History
63%
19 Minutes
5.15 GB
Other analyses. Due to space limitations, we present complexity analysis, experimental setups, and further experimental results in C
5 Conclusions
In this paper, introduce RAG4GFM, RAG framework tackling two critical GFM challenges: efficient knowledge updating and faithful reasoning. Leveraging a three-component architecture, RAG4GFM achieves significant gains in knowledge-update efficiency and reasoning faithfulness over traditional parameter-updating approaches, as demonstrated by extensive empirical evaluation across diverse domains. In future work, we will focus on: scalability billion-node graphs and real-time systems via disk-based ANN (c.g., DiskANN) and asynchronous fusion; and broader directions, including leveraging cogatiiv/contrastivv knowledge refine decision boundaries and extending the framework to multimodal graphs that include images. Our goal is to promote responsible AI practices the development and deployment of RAG-enhanced GFMs.
Acknowledgments and Disclosure of Funding
This research is supported by the Open Project of Key Laboratory of Industrial Software Engineering and Application Technology, Ministry of Industry and Information Technology (HK202403641) 1), the National Science Foundation of China under Grant 621 621252 the Zhejiang Provincial Natural Science Foundation of China under Grant I.Q24F020019, and the Hangzhou Key Research and Development Program under Grant 2025SZDIA03.
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Appendix
A
Model analysis
A.1 Complexity Analysis
Computational efficiency is crucial for the practical deployment of RAG4GFM in large-scale graph scenarios. This section analyzes the time complexity and space complexity of its key components.
Multi-level Graph Indexing: The HNSW-based hierarchical graph index is constructed with time complexity O(N log M), where N is the number of graphs or nodes. The index occupies ( space, with constants determined by parameters such as the maximum connection number M (typically M E [16, 64).
Task-aware Retriever: For a single query, the hierarchical retrieval process operates in O N) timesignificantly faster than the ((N) cost of linear searchwhile requiring only O(k) auxiliary space for & retrieved candidates (typically k < N).
Graph Fusion Enhancement: In the fusion stage, attention-baser feature aggregation has time complexity O(k (d), where [V] is the average number nodes per retrieved subgraph and d the feature dimension. The subsequent structural enhancement costs (k)[V/²)) for dense graphs or O(k when sparse adjacency lists are used. The dominant space cost stems from storing the fused graph representation, typically (((V)2) for dense adjacency.
Overall Pipeline: Combining these components, the per-query time complexity is O(log N + for dense and Oloog N + k:| Fl)) for sparse graphs. The total space complexity is O(N + [V]*, comprising the index storage (O(N) and the fused graph representation (O([V)²))
B Experimental Setups
B.1
Datasets
We evaluate on diverse datasets spanning e-commerce, synthetic, molecular, and knowledge graph domains. (1) Books-Childres Books-History and Ele-Computers and Ele-Photo 48] Amazon
co- co-purchase graphs of books and electronics. (2) MiniGCDataser 49 and BA2MotifDataser (50] are
synthetic datasets used by Barabasi-Albert model. (3) QM7bDataset [53 64 contains 7,000 stable organic molecules with computed quantum properties. (4) WNI8Datasel [52) is multi-relational
dataset extracted from Wordnet. (5) Chameleon 51 65
is wikipedia page-page network.
B.2 Graph Foundation Models
We used seven representative GFMs backbones for experiments: (1) OpenGraph [35]: aims to establish an open-domain GFM exhibiting strong zero-shot generalization by distilling LLM knowledge and employing unified graph tokenizer adaptable unseen graph structures. (2) AnyGraph [I7] addresses the challenge of graph heterogeneity by proposing a ofxture--of-Experee GNN architecture with automated routing, enabling efficient generalization and adaptation across diverse graph domains. (3) GraphGPT [16]: focuses on aligning LLMs with graph structures through instruction tuning, thereby enabling LLMs to comprehend, reason about, and generalize across various graph-based tasks in a zero-shot manner. (4) HiGPT [6I]: extends the L.MM alignment concept specifically to heterogencous graphs, utilizing instruction tuning techniques and a context- aware tokenizer to make LLMs proficient in understanding diverse node and relation types. (5) GraphAdapter [63]: introduces a parameter-efficien approach to integrate graph reasoning into frozen LLMs by inserting and fine-tuning lightweight GNN adapter modules, bridging structural and textual information without full retraining.
(6) LLaGA 622 enables frozen LLMs to directly ingest and process graph information by transforming graph structures into specialized node sequences via templates and mapping them through a versatile projector into the LLM''s embedding space. (7) GLEM [60]: proposes synergistic co-learning framework based on expectation-maxxiizzaionn allowing GNNs and LMs to iteratively enhance each other's representations and predictions on text-attributed graphs
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B.3 Settings and Parameters
To evaluate the enhancement provided by RAG4GFM to GFMs, we adopted zero-shot evalua- tion protocol. Under this protocol, GFMs perform inference test datasets without task-specific fine-tuning, leveraging contextual information supplied by RAG4GEM Specifically, during its retrieval stage, RAG4GFM identifies and provides the three subgraphs yielding the highest relevance probabilities to inform the GFM's subsequent processing for each task instance.
Performance is quantified using standard metrics: Accuracy (Acc), Recall, and ROC-AUC are employed for classification and link prediction tasks, while Mean Squared Error (MSE) and the Coefficient of Determination (R²) are applied for regression tasks.
All experiments are conducted A6000 GPU, and 512GB of RAM. The software environment comprised Ubuntu 22.04, Python 3. 30 PyTorch 2.2, and DGL 2.3.
server equipped with AMD EPYC 7B12 CPU, NVIDIA
C Further Experimental Results
C.1 Effectiveness of RAG-Enhanced GFMs
The experimental results are shown in Table Among them, the missing model data indicates that this model does not support this task.
Table 5: Results Graph Classification Tasks on Two Datasets
MiniGCDataset
BAZMotifDataset
Model
Acc
ROC-AUC
Recall
Acc
ROC-AUC
Recall
HiGPT
62.4
47.86
44.03
46.33
44.98
40.06
RAGGG-RAGAGEM
64.71
53.32
51.89
49.28
49.22
44.91
GraphGPT
60.98
48.03
44.45
44.71
44.25
39.37
GraphGPT-RAG4GFM
67.61
52.95
52.70
50.30
49.87
46.47
GLEM
56.58
45.51
40.69
44.57
42.63
37.61
GLEM-RAG4GFM
61.76
50.27
48.83
47.41
48.55
43.49
AnyGraph
58.67
46.25
42.78
47.53
45.88
40.50
AnGGraph+RAGHGFM
65.51
51.88
51.27
48.54
50.23
46.31
Table 6: Supplementary Results for Node Regression, Link Classification, and Graph Regression
Tasks
Chameleon (Node Reg.)
WN18Dataset (Link Class.)
QM7bDataset (Graph Reg.)
Model
MSE
Acc
Recall
ROC-AUC
MSE
R
AnyGraph
6.83
0.55
68.50
63.27
72.94
10.52
0.65
AnyGraph+RAGAGFM
1.47
0.63
73.05
68.75
77.40
3.42
0.73
reporss graph-classffictioon results on MiniGCDatasee and BA2MotifDataset, and Table marizes supplementary results on node regression (Chameleon). link prediction (WN18), and graph regression (QM7b). Across all backbones listed in Table integrating RAG4GFM consistently im- proves Accuracy, ROC-AUC, and Recall. For example, MiniGCDataset, AnyGraph-RAG4GFM increases Accuracy from 58.67% % to 65.51%. %, and GraphGPT+ Similar gains appear BA2Moti/Dataset (e.g., GraphGPT+RAG4GFM raises ROC-AUC from 44.25% to 49.87%). These results indicate that the retrieved subgraphs provide complementary structuraa/semmaniic
from 60.98% to 67.61 %.
that strengthen graph-level representations.
For the additional tasks in 6 RAG4GFM reduces MSE and increases R2 on Chameleon (node regression) and QM7b (graph regression). and improves all metrics on WN18 (link prediction). These trends are consistent with our findings on classification and support the conclusion that retrieval-based augmentation enhances both local (node-level) and global (graph-level) predictive signals.
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Overall, the additional tasks corroborate the broad applicability of RAG4GFM and its ability to dynamically enrich GFMs with external graph-structured knowledgedireclly addressing the static- knowledge bottleneck highlighted in the introduction.
C.2 Hyperparameter Robustness Analysis
We evaluate the robustness and deployabilityy of RAG4GFM by examining three key hyperparame- tersretrieval size (K), feature fusion threshold (7), and edge addition threshold (6).
Effect of Retrieval Size K. We study the impact of K in 21, 4,5} and summarize the results in Figure 4 Performance improves steadily as K increases from 1 to 3, since additional retrieved subgraphs enrich contextual reasoning. Beyond this point, accuracy plateaus or slightly declines because excessive retrieval introduces redundancy and noise. Balancing accuracy and efficiency, set K- = 3 as the default all experiments.
Computes (Nod; Clsss)
Histtry [Linc Pidd
WrrdCbbnsses töraph Clus.))
24.02
15.51
06.75
$2.05
Accuracy (%)
53.99
3
4
(Number Retrieved Graphs)
Figure 4: Performance of RAG4GFM with different numbers of retrieved subgraphs (K).
Sensitivity to Feature and Structure Fusion Thresholds.
We further investigatethe sensitivity of RAG4GFM to the feature fusion relevance threshold and the structural pruning threshold 8. Both are varied within {0.3, 0.5, 0.7, 0.9} using Any- Graph+RAG4GFM as representative backbone on node classification task.
AnyGraph achieves its best accuracy when and around 0.5, indicating balanced trade-off between incorporating sufficient external evidence and filtering noise. Very low thresholds (0.3) allow irrelevant re- trievals, while overly strict ones (0.9) remove useful structural links, leading slight degradation. These results show that RAG4GFM robust to small param- eter variations and requires minimal tuning.
Table 7: Sensitivity of RAG4GEM to feature (y) and structure (6) fusion thresholds. Node classification accuracy (%).
A/5
0.5
0.9
0.3
76.23
77.89
78.12
75.67
0.5
78.45
79.56
79.28
76.91
0.7
78.73
79.14
78.85
76.34
0.9
75.12
76.47
75.98
73.89
AnyGraph achieves its best accuracy when 7 and å are around 0.5, indicating a balanced trade-off between incorporating sufficient external evidence and filtering noise. Very low thresholds (0.3) allow irrelevant retrievals, while overly strict ones (0.9) remove useful structural links, leading to slight degradation. These results show that RAG4GFM is robust to small parameter variations and requires minimal tuning.
Across all hyperparameters, RAG4GFM demonstrates stable behavior and clear optimal regions. Moderate settings (c.g., K 3, = å 0.5) achieve strong accuracy without costly parameter

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search, validating the framework's 's robustness and practicality for dynamic graph environmentsdirecty addressing the scalability and maintainability challenges highlighted the introduction.
C.3
Case Study: Link Prediction in Product Co-Purchase Networks
To concretely illustrate the efficacy of RAG4GFM 's retrieval and fusion mechanisms for link prediction, we present case study the Amazon Computers dataset. In this dataset, nodes represent products, and an edge between two products indicates they are frequently bought together. Product features are derived from rag-of-words representations of their reviews. The task is to predict whether two products, not currently linked, are likely to be co-purchased.
Scenario. Consider two products:
"SpectrePro X15 Ultrabook" Ultrabook"revieess reviewws emphasize portability, sleek design, and long battery life, suggesting usage by mobile professionals.
Product A:
Product B: "PowerStation Multi-Port Hub" reverss highlight multiple ports and suitability for complex desktop setups, often used for stationary, begh-performance workstations.
A base GFM relying solely these textual features predicts low probability of co-purchase, since the two items appear to target distinct user needs.
Contextual Graph Retrieval. RAG4GFM performs retrieval over the existing Amazon Computers co-pur co-purchase graph to find contextual subgraphs for the pair (A, B). For Product A, retrieved subgraphs include portable accessories (e.g., "wireless mouse", "laptop sleeve") and similar thin-and-light ultra- books. For Product B, they include desktop monitors, ergonomic keyboards, and other workstation components. Crucially, RAG4GFM also retrieves bridging subgraphs connecting ultrabooks that are frequently co- purphasedd with docking stations or multi- mulli-port hubs. These subgraphs reveal usage patterns where high-end ultrabooks often serve dual rolesas portable devices and central elements of larger workstation setups. Such retrieved evidence enriches the context beyond the isolated features of A and B.
Information Fusion for Enhanced Representation. The fusion module integrates the original node features of the "SpectrePro X15 Ultrabook" and the "PowerStation Hub" with the structural and semantic cues from the retrieved subgraphs. This allows the GFM to recognize that, despite distinct marketing positions, both products cater to overlapping user behaviors requiring mobility and connectivity. The fused representation refines node embeddings and relational cues, bridging the gap between portable and stationary usage scenarios.
Model Inference. With this context-enriched representation, the GFM within RAG4GFM re-evaluates the CO- co-purchase likelihood for the product pair and assigns a significantly higher probability of link. By leveraging retrieved graph context, the model overcomes superficial feature dissimilarity and bases its prediction evidence of similar co-purchase behaviors found in the broader network.
Insight. This case study demonstrates that by dynamically retrieving and fusing relevant subgraphs, RAG4GFM enables GFMs to uncover non-obvious relationships and make more accurate link predictions. It highlights the ability to move beyond direct node attributes and leverage network-leve patternsan essential capability for understanding complex co- co-purchase dynamics in real-world graph applications.
25


Reviews:
[{"review_id": "4mml8zgfp7", "reviewer": "Reviewer_3of8", "summary": "The 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.", "questions": "1. 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?", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths \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.", "was_revised": true, "final_justification": "The author has resolved my questions. I suggest accepting the article.", "rebuttal": "# W1\n1. Thank you for your valuable suggestion. We acknowledge that while HNSW provides efficient $O(\\log_2 N)$ complexity, there remains room for optimization in specific scenarios.\n2. We emphasize that the RAG4GFM framework is designed to be **modular**. This means our proposed Multi-level Graph Indexing module can be flexibly replaced with more targeted indexing structures. Beyond HNSW, our envisioned solutions include:\n   - **Ultra-large scale dataset solutions**: For scenarios where indices cannot be fully loaded into memory, we can integrate **DiskANN** [1]. This approach is specifically designed for efficient ANN search on disk media such as SSDs, addressing HNSW's over-reliance on memory through optimized graph structures and I/O strategies.\n   - **Balanced and composite solutions**: We can employ composite indices represented by **IVFFlat** [2]. Compared to HNSW, IVFFlat typically offers faster construction speed and lower memory consumption. While slightly inferior in peak query speed, it provides an excellent choice for applications requiring balance between resources and performance. Furthermore, IVFFlat can be combined with **Product Quantization (PQ)** [3] to further compress vectors, achieving higher memory efficiency.\n3. In this work, we chose HNSW for its proven universality and excellent performance across various vector retrieval tasks, which is appropriate for building a foundation framework with broad applicability.\n4. We will include discussion of different indexing structures in the revised version of our paper and identify this as a promising direction for future exploration.\n\n[1] Jayaram Subramanya, Suhas, et al. \"Diskann: Fast accurate billion-point nearest neighbor search on a single node.\" Advances in neural information processing Systems 32 (2019).\n\n[2] Johnson, Jeff, Matthijs Douze, and Hervé Jégou. \"Billion-scale similarity search with GPUs.\" IEEE Transactions on Big Data 7.3 (2019): 535-547.\n\n[3] Jegou, Herve, Matthijs Douze, and Cordelia Schmid. \"Product quantization for nearest neighbor search.\" IEEE transactions on pattern analysis and machine intelligence 33.1 (2010): 117-128.\n# W2\nThis is an excellent direction for future development. In the current work, our core objective is to establish a general RAG paradigm for GFMs, addressing the fundamental challenges they face when processing graph-structured data. Therefore, we have not considered other modalities such as images.\n\nOur framework already possesses preliminary capabilities for handling multimodal data. For instance, our multi-level indexing mechanism inherently fuses semantic features and structural features of graph data.\n\nRegarding the integration of visual information suggested by the reviewer, our framework can incorporate a **visual encoder** to integrate visual features and align them with graph embeddings and text embeddings in a unified semantic space for hybrid indexing. Additionally, our designed attention-based fusion enhancement mechanism can naturally accommodate additional modalities. Therefore, overall, our current framework can naturally support image data through the addition of a visual encoder.\n\nWe greatly appreciate your suggestion and will explicitly identify in the \"Future Work\" section of the revised version that extending RAG4GFM to broader multimodal scenarios represents an important research direction.\n# W3\nThank you for raising this important practical application consideration.\n\nAs foundational work aimed at validating the feasibility of a new paradigm, our current primary objective is to ensure the framework's generality and effectiveness. We have incorporated efficiency considerations into our design. As mentioned in Sections 3.1, 3.3, and Appendix A.1 of our paper, the current model primarily ensures baseline performance through efficient HNSW indexing and sparse matrix operations. Nevertheless, we acknowledge that in-depth optimization research for real-time response scenarios is indeed insufficient. Based on your suggestion, we have explored several possible implementations:\n\n**Retrieval Phase Optimization**:\n   - **Hardware acceleration and index compression**: Fully leverage GPU parallel computing and employ techniques such as Product Quantization to compress indices, reducing computational load and I/O overhead during retrieval.\n   - **Result caching and prefetching**: For high-frequency query patterns, establish an LRU cache layer to directly return cached retrieval results.\n\n**Fusion Phase Optimization**:\n   - **Model quantization and distillation**: Quantize features and model parameters in the graph fusion module.\n   - **Asynchronous fusion and updates**: Design the knowledge retrieval and fusion processes as background asynchronous tasks.\n\nWe will thoroughly explore these real-time optimization strategies in future work and explicitly identify this important research direction in the conclusion section of the revised version.\n# Q1\n1. This is an excellent technical question. We apologize for not clearly explaining the weight design in the **indexing construction** and **retrieval** phases, and we deeply regret this oversight.\n\n2. First, during the **indexing construction** phase, our core principle is to **preserve all dimensional information without loss**. Therefore, we do not apply any pre-weighting or fusion to text, structural, edge, and graph-level features. Instead, we construct **independent, parallel indexing spaces** for each feature type, thereby completely preserving their respective information.\n\n3. Second, the \"importance\" of features is dynamically and adaptively manifested in the subsequent **retrieval** phase based on specific tasks, without requiring adjustment of feature weights for different types of graph data. Our task-aware retriever first determines the query type, then decides which indexing space(s) to retrieve from. For example, a node classification task will simultaneously query both text semantic indices and structural indices, then integrate the two sets of results through the RRF algorithm, which implicitly balances the contributions of different features according to task requirements.\n# Q2\nThank you for this very practical question.\n\nOur current approach employs a LLM-based classifier (based on the text-embedding-ada-002 model) that determines retrieval strategies by computing semantic similarity between user queries and predefined task templates (such as node classification, link prediction, graph classification, etc.).\n\nWe acknowledge insufficient discussion of its performance in our draft. Considering that current GFM task types are limited and well-defined, we judged LLM-based classification as mature and reliable, focusing research efforts on more challenging graph knowledge retrieval and fusion aspects.\nGiven that the capability boundaries of current GFMs are primarily concentrated on these limited standard graph learning tasks, when users pose queries beyond this scope, even if the classifier works perfectly, the underlying GFM itself cannot provide effective solutions. Therefore, we adopt a strategy of directly prompting users with \"task not supported.\"\n\nTo more precisely address your concerns, we manually annotated 1,000 node classification instances from the Computers dataset for validation within the limited time available, and tested using the text-embedding-ada-002 model. The results demonstrate excellent classifier performance: the average latency for classifying a single query is only 3.54 milliseconds, and the classification accuracy on these 1,000 data instances reached 100%.\n# Q3\nYour question touches upon one of the core motivations of our research. We need to honestly acknowledge that, as foundational work introducing the RAG paradigm to the GFM domain for the first time, our current primary objective is to validate the feasibility and general effectiveness of this new paradigm. Therefore, we have indeed given insufficient consideration to dynamic update scenarios in actual production environments.\n\nWhile we have not specifically designed streaming graph experiments, our current experimental design simulates \"knowledge update\" scenarios to some extent. We deliberately selected datasets (such as the TAG benchmark) that were not used during GFM pre-training for testing. This process essentially simulates the core idea of models rapidly adapting and solving problems through external retrieval when encountering entirely new knowledge environments. The results in our paper demonstrate the effectiveness of RAG4GFM under this simulated scenario. Additionally, we supplemented with comparative experiments on knowledge update efficiency, comparing RAG4GFM with parameter-efficient fine-tuning methods such as GraphLoRA on the same tasks. The results show that our method achieves similar performance while demonstrating faster knowledge update speed and significantly reduced memory consumption, which indirectly proves the advantages of our framework in rapid knowledge updates.\n\n\n\n|          | GFM      | Dataset   | Target Acc | Time        | Peak GPU Memory |\n|----------|----------|-----------|------------|-------------|-----------------|\n| GraphLoRA| AnyGraph | Computers | 78%        | 7.32 Hours  | 25.23 GB        |\n| RAG4GFM  | AnyGraph | Computers | 78%        | 63 Minutes  | 9.86 GB         |\n| GraphLoRA| HiGPT    | History   | 63%        | 5.87 Hours  | 17.18 GB        |\n| RAG4GFM  | HiGPT    | History   | 63%        | 19 Minutes  | 5.15 GB         | \n\n\nHowever, we agree that we currently lack experiments on true streaming or dynamic graph scenarios. This is a critical direction that we must explore in-depth in our future work. We will explicitly state this limitation in the conclusion of our revised manuscript."}, {"review_id": "GlwvIQlngM", "reviewer": "Reviewer_L6fj", "summary": "The 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.", "questions": "Please address my concernslisted above.", "limitations": "YES", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths \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.", "was_revised": true, "final_justification": "My question has been resolved and I recommend accepting this paper.", "rebuttal": "# W1\nThank you for this very constructive suggestion. In our initial draft, due to the massive computational overhead of conducting multiple experimental runs across 7 different GFM backbone models and various tasks, we were unable to include error bars. Based on your recommendation, we have urgently completed supplementary experiments. We selected three GFM models (HiGPT, GLEM, AnyGraph) and re-ran experiments three times on two datasets (Computers, History), calculating standard deviations for performance metrics. The results are shown in the table below:\n\n\n| Model | Computers |  |  | History |  |  |\n|-------|-----------|-----------|-----------|---------|-----------|-----------|\n|  | Acc | ROC-AUC | Recall | Acc | ROC-AUC | Recall |\n| HiGPT+RAG4GFM | 79.39 ± 1.01 | 66.29 ± 0.78 | 65.29 ± 1.29 | 62.91 ± 0.98 | 64.05 ± 0.75 | 59.04 ± 1.31 |\n| GLEM+RAG4GFM | 82.98 ± 0.78 | 68.68 ± 0.87 | 68.73 ± 0.95 | 61.29 ± 1.15 | 60.11 ± 0.69 | 56.84 ± 0.75 |\n| AnyGraph+RAG4GFM | 78.89 ± 0.98 | 66.36 ± 0.93 | 65.01 ± 1.22 | 63.39 ± 0.91 | 64.65 ± 0.88 | 60.39 ± 0.89 |\n\n\nFrom the experimental results, the average performance across multiple runs is highly consistent with the single-run results reported in our initial draft, demonstrating the stability and reproducibility of our experimental results. Furthermore, all experiments maintain small standard deviations, indicating that our proposed RAG4GFM framework exhibits stable performance.\n\n# W2\nThank you very much for this insightful observation. RAG4GFM's performance being influenced by the capabilities of underlying GFMs and LLMs is indeed an inherent challenge that any RAG system faces. However, we believe this dependency is effectively mitigated in our framework.\n\nFirst, the \"retrieval-augmentation\" paradigm of RAG can substantially suppress potential biases or hallucinations that underlying models might generate based solely on their internal parametric knowledge, by providing authentic and verifiable external data for the model's reasoning process. \n\nSecond, we employ a **modular design** that ensures our framework can be combined with different GFMs or LLMs without being bound to specific models. This means that as more powerful GFMs and LLMs emerge in the future, our framework can directly integrate them and benefit from their performance improvements. \n\nFinally, we conducted extensive experiments in our paper: to demonstrate the universality of our framework, we selected up to **seven GFMs with diverse architectures** for comprehensive experiments. The results consistently show that RAG4GFM brings performance improvements to all these models, proving the broad effectiveness of our approach.\n\n# W3\nThank you for your valuable suggestion regarding theoretical depth. As the first work to systematically introduce the RAG paradigm to the GFM domain, our primary research focus was on the framework's innovative design and its extensive empirical validation. While providing a complete, novel theoretical proof for RAG4GFM on short notice is challenging, the effectiveness of our framework is highly consistent with existing theoretical analyses in the broader RAG research field.\n\n\n#### Alignment with Existing RAG Theory\n\n**1. On Knowledge Integration:**\nRecent theoretical work models RAG systems as cascaded information channels, where each component (query encoding, retrieval, context integration, generation) acts as a distinct information-theoretic channel with a measurable capacity[1]. Our **Multi-level Graph Indexing** is designed precisely to maximize the information capacity of these channels. Furthermore, our attention-based feature fusion mechanism adheres to the theoretical principle of \"context-adaptive weighting\" rather than simple information concatenation. A key innovation of our work, the fusion of topological structure, extends this integration theory from one-dimensional text to high-dimensional graph structures, ensuring that the graph's connectivity patterns are effectively absorbed and utilized as a core form of knowledge.\n\n**2. On Enhancing Reasoning Fidelity:**\nA significant body of recent research suggests that a root cause of hallucination in LLMs is the high uncertainty (or entropy) in their generation process when faced with knowledge-intensive tasks[2]. By providing concrete, relevant external knowledge to condition the generative model, RAG dramatically reduces the model's predictive uncertainty (i.e., lowers complexity or perplexity) for a given task[3]. This reduction in uncertainty, in turn, greatly improves the fidelity and reliability of the output. RAG has also been theorized as an effective means of augmenting a model with non-parametric memory, efficiently decoupling the model's learned general reasoning abilities (parametric memory) from specific, updatable facts in an external knowledge base (non-parametric memory).\n\n#### Existing Theoretical Underpinnings\n\nOur current framework design also incorporates components with established theoretical support:\n* The **Multi-level Graph Indexing** module's use of the HNSW algorithm comes with a theoretical complexity guarantee of $O(\\log_2 N)$.\n* The **Laplacian Positional Encoding (LAPPE)** we use is grounded in solid spectral graph theory and effectively captures the structural information of nodes.\n\n#### Future Plan\n\nFollowing your suggestion, we will add a new discussion chapter in the appendix of our revised manuscript. This section will delve deeper into the connection between our graph fusion mechanism and attention theory. It will also elaborate from an information-theoretic perspective on why \"evidence-based reasoning\" effectively reduces model uncertainty and, thus, hallucination. We hope this will partially address your expectations regarding theoretical depth.\n\n[1] Zhao, Siyun, et al. \"Retrieval augmented generation (rag) and beyond: A comprehensive survey on how to make your llms use external data more wisely.\" arXiv preprint arXiv:2409.14924 (2024).\n\n[2] Gao, Yunfan, et al. \"Retrieval-augmented generation for large language models: A survey.\" arXiv preprint arXiv:2312.10997 2.1 (2023).\n\n[3] Shuster, Kurt, et al. \"Retrieval augmentation reduces hallucination in conversation.\" arXiv preprint arXiv:2104.07567 (2021).\n\n# W4:\nWe sincerely thank the reviewer for pointing out these typographical and grammatical errors. We are very grateful for your meticulous and rigorous review and sincerely apologize for these oversights.\n\nWe promise that in the final version, all the errors you have listed (e.g., \"Retriver\" -> \"Retriever\") will be corrected. Furthermore, we will conduct a thorough professional proofreading of the entire manuscript to ensure the accuracy and professionalism of the language and to improve the overall readability of the paper."}, {"review_id": "zU5GNjkHfe", "reviewer": "Reviewer_yibP", "summary": "This 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.", "questions": "1. 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?", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": true, "final_justification": "In the rebuttral discussion, the authors have clarified the issues I raised and the additional experiments have addressed my concerns, so I consider a moderate upward adjustment to my rating score.", "rebuttal": "# W1:\nThank you for pointing out this critical issue. We acknowledge that our initial draft did not comprehensively discuss its performance.\n\nThis was primarily because, considering that the task types currently processable by GFMs are limited and well-defined, we initially judged that utilizing language models for classification among a small number of limited tasks is a relatively mature and reliable component. \n\nTo more precisely address your concerns, we manually annotated 1,000 node classification instances from the Computers dataset for validation within the limited time available, and tested using the text-embedding-ada-002 model. The results demonstrate excellent classifier performance: the average latency for classifying a single query is only 3.54 milliseconds and the classification accuracy reached 100%.\n\nTo further address your concern about the \"impact of its classification errors,\" we deliberately assigned incorrect classifications to these 1,000 samples (classifying them as edge-level or graph-level tasks) for experimentation. The experimental results are shown in the following table:\n\n| Model | Acc | ROC-AUC | Recall |\n| --- | --- | --- | --- |\n| HiGPT+RAG4GFM | 79.34 | 66.89 | 66.42 |\n| HiGPT+RAG4GFM+Wrong Strategy | 77.47 | 63.22 | 62.50 |\n| AnyGraph+RAG4GFM | 79.56 | 66.12 | 65.34 |\n| AnyGraph+RAG4GFM+Wrong Strategy | 75.50 | 61.49 | 60.31  |\n\nThis further confirms our initial judgment: for the limited task types that current GFMs can handle, LLM-based classifiers are already very mature and reliable. Even with certain classification errors, the overall system maintains good robustness.\n# W2\nWe thank the reviewer for this very pertinent critique. We have urgently added a supplementary experiment. The results are summarized as follows:\n\n\n|          | GFM      | Dataset   | Target Acc | Time        | Peak GPU Memory |\n|----------|----------|-----------|------------|-------------|-----------------|\n| GraphLoRA| AnyGraph | Computers | 78%        | 7.32 Hours  | 25.23 GB        |\n| RAG4GFM  | AnyGraph | Computers | 78%        | 63 Minutes  | 9.86 GB         |\n| GraphLoRA| HiGPT    | History   | 63%        | 5.87 Hours  | 17.18 GB        |\n| RAG4GFM  | HiGPT    | History   | 63%        | 19 Minutes  | 5.15 GB         | \n\n\nTo achieve similar or even better performance levels, RAG4GFM only needs a small amount of time to complete indexing and retrieval construction for new knowledge, with computational resource consumption throughout the entire process being far lower than GraphLoRA.\n# W3\nThank you for raising concerns about the noise issue. This is indeed a critical consideration for RAG-type methods.\n\nAs this is the first work to establish a RAG paradigm in the GFM domain with graph data scenarios, we aimed to pursue a relatively general and flexible preliminary framework, and therefore did not specifically propose a dedicated module for identifying introduced noise.\n\nNevertheless, we have designed several noise-filtering information screening mechanisms in the RAG4GFM framework, primarily in three aspects:\n   - **Task-Aware Reranking**: Before fusion, our retriever reranks retrieved candidates based on comprehensive relevance to the query (Equation 3). This ensures that the most relevant graph knowledge is prioritized.\n   - **Attention-based Fusion**: In the graph fusion module, we dynamically allocate weights $\\alpha_i$ to each retrieved graph through attention mechanisms. Even if a low-quality graph is retrieved, it will receive minimal attention weight, minimizing its impact on the final results.\n   - **Threshold Filtering**: The thresholds $\\gamma$ and $\\delta$ serve to thoroughly eliminate low-relevance information.\n\nWe have provided a preliminary case study in Appendix C.3 for your intuitive understanding.\n# Q1\nThank you for this insightful question about our feature engineering details. \nOur theoretical motivation for concatenating LAPPE with node in-degree and out-degree stems from the important principle of global-local information complementarity in modern graph representation learning. Existing research[1,2] shows that standard Laplacian positional encoding primarily contains global structural information rather than local structural information, limiting its performance in graph learning tasks requiring local structures.\nSpecifically, LAPPE captures global positional information by embedding graphs into Euclidean space through eigendecomposition of the graph Laplacian[3], while in-degree and out-degree serve as simple local centrality measures reflecting node connectivity within neighborhoods[4]. The classic Graphormer[5] model adopts similar ideas, combining centrality encoding (based on node degrees) and spatial encoding to capture orthogonal and complementary structural information with excellent performance.\n\n\nTo quantitatively validate our design choice, we conducted a detailed ablation study.The results are summarized below:\n\n\n| Model | Feature Encoding Method | Computers - ACC | Computers - ROC-AUC | Computers - Recall | History - ACC | History - ROC-AUC | History - Recall |\n|---------------------|-------------------------|-----------------|---------------------|--------------------|---------------|-------------------|------------------|\n| AnyGraph+RAG4GFM | LAPPE only | 72.18 | 58.42 | 59.87 | 56.73 | 56.78 | 52.36 |\n| AnyGraph+RAG4GFM | Node Degree only | 68.94 | 55.21 | 56.29 | 54.12 | 53.45 | 49.78 |\n| AnyGraph+RAG4GFM | LAPPE + Node Degree | 79.56 | 66.12 | 65.34 | 63.45 | 64.12 | 60.23 |\n| HiGPT+RAG4GFM | LAPPE only | 74.26 | 60.15 | 61.54 | 58.89 | 58.94 | 54.12 |\n| HiGPT+RAG4GFM | Node Degree only | 70.17 | 56.73 | 57.83 | 55.67 | 54.81 | 51.45 |\n| HiGPT+RAG4GFM | LAPPE + Node Degree | 79.34 | 66.89 | 66.42 | 63.2 | 63.45 | 59.76 |\n\n\nThis experiment provides strong evidence that these two feature types provide complementary information, thus justifying our design choice. \n\n[1] Hoang, Thi Linh, and Viet Cuong Ta. \"Balancing structure and position information in graph transformer network with a learnable node embedding.\" Expert Systems with Applications 238 (2024): 122096.\n\n[2] Cantürk, Semih, et al. \"Graph positional and structural encoder.\" arXiv preprint arXiv:2307.07107 (2023).\n\n[3] Flores, Alejandro. Laplacian Positional Encoding. afloresep.github.io, 8 Oct. 2024, https://afloresep.github.io/posts/2024/10/laplacian_positional_encoding/. Accessed 31 July 2025.\n\n[4] Metcalf, Leigh, and William Casey. \"Chapter 5 - Graph Theory.\" Cybersecurity and Applied Mathematics, edited by Leigh Metcalf and William Casey, Syngress, 2016, pp. 67–94. https://doi.org/10.1016/B978-0-12-804452-0.00005-1. Accessed 31 July 2025.\n\n[5] Ying, Chengxuan, et al. Do Transformers Really Perform Bad for Graph Representation? arXiv, 2021, https://arxiv.org/abs/2106.05234. Accessed 31 July 2025.\n# Q2\nThis is an excellent suggestion, and we acknowledge that we did not give this aspect sufficient attention in our initial experimental design.\n\nFollowing your recommendation, we have added the parameter sensitivity analysis you suggested.The results are summarized in the table below:\n\n| | AnyGraph+RAG4GFM | | | | HiGPT+RAG4GFM  | | | |\n|-------|------------------|----------|----------|----------|----------------|----------|----------|----------|\n|   γ\\δ   | 0.3 | 0.5 | 0.7 | 0.9 | 0.3 | 0.5 | 0.7 | 0.9 |\n| 0.3 | 76.23 | 77.89 | 78.12 | 75.67 | 76.89 | 78.23 | 78.67 |\n| 0.5 | 78.45 | **79.56** | 79.28 | 76.91 | 78.91 | 79.34 | 79.08 |\n| 0.7 | 78.73 | 79.14 | 78.85 | 76.34 | 79.17 | 79.52 | 78.94 |\n| 0.9 | 75.12 | 76.47 | 75.98 | 73.89 | 75.78 | 77.12 | 76.83 | \n\nThe experimental results indicates that **RAG4GFM** has a relatively low dependency on meticulous parameter tuning, highlighting its practical utility.\n\nWe will add these detailed sensitivity analysis results, including validation on different datasets and guidelines for parameter selection, to the appendix of our revised manuscript.\n# Q3\nThank you for your extremely meticulous review, and we sincerely apologize for this oversight! The inconsistency you observed is indeed due to differences in evaluation settings, which we failed to clearly explain in our initial draft, and we deeply regret this confusion.\nThe ablation study results in Figure 3 (88.5%) were conducted during the early stages of our research using a randomly selected subset (20% of the data) from the Computers dataset for rapid validation experiments, aimed at quickly assessing the relative importance of each component. In contrast, the results in Table 2 (79.56%) represent formal experiments conducted on the complete dataset following standard evaluation protocols.\nWe recognize that this inconsistency seriously undermines the credibility and reproducibility of our work. Such methodological discrepancies are unacceptable in rigorous academic research, and we take full responsibility for this oversight. To rectify this critical issue, we have completely re-run all ablation experiments on the full Computers dataset using identical experimental protocols and evaluation metrics as those used for Table 2.\nThe corrected ablation study results on the complete dataset are as follows:\n\n\n| Model   | Computers (Node Class.) | | Computers (Link Pred.) | | MiniGCDataset (Graph Class.) |          |\n|---------|--------------------------|----------|-------------------------|----------|------------------------------|----------|\n| | Acc| ROC - AUC | Acc | ROC - AUC | Acc | ROC - AUC |\n| w/o RAG | 73.45 | 60.89 | 52.17 | 38.53 | 58.67 | 46.25 |\n| w/o GF  | 76.23 | 63.41 | 55.48 | 41.76 | 62.18 | 48.94 |\n| w/o GI  | 77.89 | 64.78 | 56.92 | 43.15 | 63.73 | 49.67 |\n| RAG4GFM | 79.56 | 66.12 | 58.26 | 44.92 | 65.51 | 51.88 |\n\nThe corrected results still clearly demonstrate the importance of the RAG framework, graph fusion module, and graph indexing components, supporting our core conclusions."}, {"review_id": "aVuk3ynRrK", "reviewer": "Reviewer_xzY6", "summary": "This 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.", "questions": "1.  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.", "limitations": "The 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.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "### 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.", "was_revised": true, "final_justification": "As I mentioned in rebuttal phase, the authors have resolved my concerns on model design, ablation study, etc, so I will raise my evaluation score", "rebuttal": "# W1\nThank you for your candid criticism. While our work builds on existing concepts, its core innovation is the first systematic adaptation of the RAG paradigm to graph-structured data and GFMs. Our framework provides an end-to-end solution to three fundamental challenges that prior methods do not address:\n\nUnified Graph Indexing: Standard text RAG is insufficient for GFM scenarios involving hybrid queries of text and structure. We designed a unified multi-dimensional index that simultaneously encodes and indexes both textual semantics and graph topology.\n\nTask-Aware Retrieval: We developed adaptive retrieval strategies with task-aware reranking mechanisms that dynamically select the optimal indices to support diverse GFM downstream tasks.\n\nGraph Knowledge Integration: Simple text concatenation is ineffective for complex graph data. We pioneered a novel two-stage Graph Fusion Enhancement module that integrates semantic features via attention and topological structures via sparse matrix operations. To our knowledge, this graph-native fusion approach is a novel contribution to both RAG and GFM literature.\n\nIntegrating these solutions into a coherent RAG framework tailored for GFMs is a pioneering contribution that establishes a new research direction.\n# W2&Q4\nWe apologize for the unclear presentation in Section 3.3. Since images cannot be added, we provide a more intuitive explanation here.\n\nThe Graph Fusion Enhancement module aims to seamlessly integrate retrieved high-quality graph knowledge into the original query graph through two key steps:\n\n**Step 1: Attention-based Feature Fusion**: Equations (4)-(6) compute attention weights $\\alpha_i$ to measure similarity between the $i$-th retrieved graph and query graph, then use these weights to perform weighted averaging of node features $\\mathbf{h}_u$ from retrieved graphs. \nThe threshold function $\\mathbf{I}_{{\\alpha_i>\\gamma}}$ filters low-quality results, ultimately fusing high-quality external features into the original node features $\\mathbf{h}_v$ to obtain enhanced representations $\\mathbf{h}'^{N}_v$, $\\mathbf{h}'_{\\text{SRC}^E}$, and $\\mathbf{h}'_{\\text{DST}^E}$.\nEquations (7)-(8) handle graph-level tasks, where $\\beta_v^i$ computes attention weights between query graph global features $\\mathbf{h}_q^g$ and retrieved graph global features $\\mathbf{h}_g^i$ via SOFTMAX.\n\n**Step 2: Topological Structure Enhancement**: Equation (9) performs structural fusion between the original query graph's adjacency matrix $\\mathbf{A}$ and retrieved graphs' adjacency matrices $\\mathbf{A}_i$. Each $\\mathbf{A}_i$ is weighted by the same attention weight $\\alpha_i$, with threshold function $\\mathbb{I}[\\alpha_i > \\gamma]$ ensuring only high-quality structural information participates in fusion, forming the enhanced adjacency matrix $\\mathbf{A}'$. \nThis mechanism emphasizes highly relevant external knowledge through dynamic weight allocation while reducing noise through threshold filtering, achieving dual enhancement of features and structure. \n# W3&Q1\nWe thank you for pointing out this critical omission in our experimental description, and we sincerely apologize for the lack of clarity.\n\nThe graphs mentioned in Figure 1, such as \"Code Graph,\" \"Wikipedia Graph,\" and \"Cite Network,\" are **not** the external knowledge bases used directly in our RAG experiments. \n\nFor our actual RAG experiments, to ensure rigor, reproducibility, and fair comparison, we adopted the following standardized method for constructing the external knowledge base:\n\n**For each specific downstream task, the corresponding external knowledge base is constructed exclusively from the *training set* of that task's dataset.**\n\nFor example, when performing the node classification task on the **Computers** dataset, we build our multi-level graph index using all nodes, features, and connections present in the **training split** of that dataset. During the inference phase, when the model needs to make a prediction for a node in the **test set**, it retrieves information from this knowledge base built from the training set. This design ensures that the knowledge retrieved by RAG is highly relevant to the task while strictly avoiding any data leakage from the test set.\n# W4&Q3\nThank you for your very reasonable request for a more in-depth ablation study. To better understand the contribution of each component, we have added the more fine-grained ablation experiments you suggested.\n\nSpecifically, we selected two GFM backbones (**AnyGraph**, **HiGPT**) and conducted node classification experiments on two datasets (**Computers**, **History**). We compared the performance of several variants, focusing on two key areas: the indexing components and the graph fusion mechanisms. The results are presented below:\n\n| Model           | Index Type               | Computers | History |\n|-----------------|--------------------------|-----------|---------|\n| AnyGraph+RAG4GFM | Graph-level index only   | 67.42     | 52.18   |\n| AnyGraph+RAG4GFM | Edge-level index only    | 70.89     | 55.67   |\n| AnyGraph+RAG4GFM | Structure index only     | 73.16     | 58.23   |\n| AnyGraph+RAG4GFM | Text index only          | 76.34     | 60.78   |\n| AnyGraph+RAG4GFM | Graph feature fusion only| 76.93     | 61.47   |\n| AnyGraph+RAG4GFM | Graph structure fusion only | 74.28   | 57.89   |\n| AnyGraph+RAG4GFM | Full Model | 79.56 | 63.45 |\n| HiGPT+RAG4GFM    | Graph-level index only   | 68.17     | 53.94   |\n| HiGPT+RAG4GFM    | Edge-level index only    | 71.52     | 56.41   |\n| HiGPT+RAG4GFM    | Structure index only     | 74.89     | 59.12   |\n| HiGPT+RAG4GFM    | Text index only          | 77.06     | 61.33   |\n| HiGPT+RAG4GFM    | Graph structure fusion only | 75.41   | 58.76   |\n| HiGPT+RAG4GFM    | Graph feature fusion only| 77.62     | 62.15   |\n| HiGPT+RAG4GFM    | Full Model | 79.34 | 63.21 |\n\n\n#### Analysis of Results\n**1. Ablation on Indexing Components:**\nCrucially, the combination of **Text Semantics and Structural indices** (part of our full model) achieves the best performance (79.56%), a **3.22 percentage point improvement** over the best single index, proving the effectiveness of fusing multimodal information at the retrieval stage.\n\n**2. Ablation on Graph Fusion Components:**\n* The **Dual Fusion of Structure and Features** (our full model, 79.56%) achieves the optimal performance, demonstrating that the two fusion mechanisms provide complementary enhancements.\n# Q2\nThank you for this valuable suggestion, which would greatly enhance the interpretability of our work. We have already included a detailed case study on link prediction in Appendix C.3 of our supplementary materials.\n\nHowever, we recognize that while this case study describes the overall process, it could be more explicit in showing how retrieved subgraphs specifically influence predictions. Therefore, we supplement with the following detailed explanation:\n\n**Retrieval Impact**: RAG4GFM discovered a key usage pattern where a high-end ultrabook (ThinkPad X1 Carbon) connects to both portable accessories (wireless mouse) and desktop peripherals (multi-port hub), providing critical structural evidence for the target product pair.\n\n**Fusion Process**: These pattern-exhibiting subgraphs received high attention weights (α=0.79), integrating their structural information into the original SpectrePro X15 and PowerStation node representations, enabling the GFM to understand user habits of switching between mobile and desktop setups.\n\n**Prediction Enhancement**: The enhanced representations increased the GFM's link prediction probability from 0.15 to 0.82, successfully identifying that these seemingly different products serve the same user group in different contexts.\n\nWe commit to adding a simplified illustrative diagram in the final version to provide readers with an at-a-glance view of how knowledge is retrieved, integrated, and impacts the model's decision-making process.\n# Q5\nWe sincerely apologize for the incomplete code package and the lack of clear instructions.\n\nOur intention was to decouple our **RAG4GFM** framework from the replaceable GFM backbones. We were concerned that bundling the GFM code with our own might obscure our core contribution from the reviewers' perspective. Therefore, we only included a note in the README file instructing users to place the downloaded GFM code into a \"GFM/\" directory. \n\nAs we are now in the rebuttal phase, we are unable to add links or update the Supplementary Material. Instead, we offer a more detailed guide to running our code below:\n\n#### Setup and Execution Guide\n1.  **Place GFM Source Code:** Place the source code of your chosen GFM backbone (e.g., AnyGraph, GraphGPT) into a folder named `GFM/` located in the project's root directory.\n2.  **Configure the Model:** In the main configuration file, set the `gfm_model` parameter to the name of the chosen model (e.g., `'AnyGraph'`).\n3.  **Run the Pipeline:** Execute the main script by running `python model/rag4gfm_pipeline.py`.\n# L1\nWe thank the reviewer and agree that our discussion of limitations was too generic. We will completely rewrite this section to be more specific.\n\nThe revised \"Conclusion and Future Work\" section will address:\n\n* **Scalability Challenges:** We will discuss the challenges of applying **RAG4GFM** to billion-node graphs and real-time systems, and explore solutions like `DiskANN` and asynchronous fusion.\n\n* **Knowledge Reliability:** We will address the need for future confidence-scoring or fact-checking modules to handle noisy or outdated \"dirty data\" from the retrieval source.\n\n* **Broader Explorations:** We will outline future research directions, including:\n    * Utilizing **\"negative\" or \"contrastive knowledge\"** to refine decision boundaries.\n    * Extending our framework to **multimodal graphs** that include images."}]

Author rebuttal:

Meta-review: The paper presents RAG4GFM, the first systematic framework that adapts Retrieval-Augmented Generation (RAG) to Graph Foundation Models (GFMs). Extensive experiments across seven GFMs and six tasks (node/edge/graph-level) corroborate these claims, showing consistent accuracy gains (≈3–10 pp) and order-of-magnitude reductions in update time and memory versus parameter-efficient fine-tuning baselines. The submission is technically solid, demonstrating high impact on graph representation learning and foundation model reliability, and has strong reproducibility. Reviewers converged on “Accept” after the rebuttal; no ethical concerns were raised.
Decision: Accept (oral)