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Retrieval-Augmented Generation with Graphs (GraphRAG)

Haoyu Han, Yu Wang, Harry Shomer, Kai Guo, Jiayuan Ding, Yongjia Lei, Mahantesh Halappanavar, Ryan A. Rossi, Subhabrata Mukherjee, Xianfeng Tang, Qi He, Zhigang Hua, Bo Long, Tong Zhao, Neil Shah, Amin Javari, Yinglong Xia, Jiliang Tang

arXiv:2501.00309v2cs.IRcs.CLcs.LG

TL;DR

GraphRAG addresses the need for a systematic account of retrieval-augmented generation over heterogeneous, relational, and domain-specific graph data. The survey proposes a five-component framework, reviews domain-specialized techniques and resources, and identifies research challenges and opportunities. It also shows how GraphRAG applications span knowledge, document, scientific, social, and other graph domains.

  • Problem

    GraphRAG lacks a systematic, up-to-date survey despite graph data's heterogeneous formats and domain-specific relational information requiring dedicated designs.

  • Method

    The survey unifies GraphRAG into five components, reviews representative and domain-specific techniques, and collects benchmark datasets and tools.

  • Results

    The survey covers GraphRAG applications across varied domains and reviews representative techniques for its framework components.

  • Takeaways & Limitations

    GraphRAG designs must account for graph structure and domain-specific relational patterns across applications.

  • Takeaways & Limitations

    Current GraphRAG generators primarily accept text, making retrieved texts, triplets, or graphs and their structural encodings difficult to align with LLM inputs.

Abstract

from arXiv · show

Retrieval-augmented generation (RAG) is a powerful technique that enhances downstream task execution by retrieving additional information, such as knowledge, skills, and tools from external sources. Graph, by its intrinsic "nodes connected by edges" nature, encodes massive heterogeneous and relational information, making it a golden resource for RAG in tremendous real-world applications. As a result, we have recently witnessed increasing attention on equipping RAG with Graph, i.e., GraphRAG. However, unlike conventional RAG, where the retriever, generator, and external data sources can be uniformly designed in the neural-embedding space, the uniqueness of graph-structured data, such as diverse-formatted and domain-specific relational knowledge, poses unique and significant challenges when designing GraphRAG for different domains. Given the broad applicability, the associated design challenges, and the recent surge in GraphRAG, a systematic and up-to-date survey of its key concepts and techniques is urgently desired. Following this motivation, we present a comprehensive and up-to-date survey on GraphRAG. Our survey first proposes a holistic GraphRAG framework by defining its key components, including query processor, retriever, organizer, generator, and data source. Furthermore, recognizing that graphs in different domains exhibit distinct relational patterns and require dedicated designs, we review GraphRAG techniques uniquely tailored to each domain. Finally, we discuss research challenges and brainstorm directions to inspire cross-disciplinary opportunities. Our survey repository is publicly maintained at https://github.com/Graph-RAG/GraphRAG/.

1 Introduction

GraphRAG extends retrieval-augmented generation to graph-structured data, whose heterogeneous formats and relational, domain-specific information require specialized designs. This survey organizes the fragmented field through a holistic framework, domain-focused reviews, resources, and future directions.

  • Motivation: GraphRAG retrieves and integrates external knowledge, skills, or tools with queries before generation, extending RAG to graph-structured sources.Graph data encodes heterogeneous and relational information through nodes connected by edges.
  • Design Challenges: Graph-structured data differs from conventional RAG data through diverse formats, interdependent information, and domain-specific relations.These differences affect retrieval, organization, generation, and graph construction across applications.
  • Research Landscape: The current GraphRAG landscape is fragmented and concentrates mainly on knowledge and document graphs, leaving broader applications comparatively overlooked.The survey identifies this imbalance as a scope limitation in existing research.
  • Survey Contributions: The survey proposes a five-component GraphRAG framework comprising a query processor, retriever, organizer, generator, and graph data source.Representative techniques are reviewed within each component.
  • Survey Contributions: The survey categorizes GraphRAG designs into 10 domains and reviews their applications, graph construction methods, component designs, datasets, and tools.It also highlights current challenges and future opportunities for advancing GraphRAG.

2 A Holistic Framework of GraphRAG

This section presents a holistic GraphRAG framework grounded in existing literature and introduces the problem setting and notation used throughout the framework.

  • The framework is presented based on existing GraphRAG literature.
  • The section introduces a holistic framework of GraphRAG.
  • The section defines the problem setting and notation used throughout the framework.

2.1 Problem Setting and Notations

GraphRAG processes a query through five stages: preprocessing, graph retrieval, content organization, generation, and interaction with a graph-structured data source. Each stage requires graph-aware design because graph data encodes relational information.

  • Pipeline: GraphRAG preprocesses the user query with a query processor before retrieval.The processed query is represented as ˆQ = ΩProcessor(Q).
  • Pipeline: The retriever obtains content from the graph data source using the preprocessed query.The retrieved content is represented as C = ΩRetriever(ˆQ, G).
  • Pipeline: The organizer arranges and refines retrieved content before generation.The refined content is represented as ˆC = ΩOrganizer(ˆQ, C).
  • Pipeline: The generator uses the processed query and refined content to produce the final answer.The answer is represented as A = ΩGenerator(ˆQ, ˆC).
  • Graph-aware Design: All five GraphRAG components require dedicated designs to handle graph-structured inputs, outputs, and operations over relational information.The paper contrasts these requirements with conventional RAG's sparse or dense encoder-based retrieval.

2.2 Task Applications and Example Query Q

GraphRAG supports queries and task applications across domains using text, molecular strings, and multimodal inputs, with domain-specific applications summarized in Table 1 and query-processing differences shown in Table 2.

  • Query Formats: GraphRAG queries may be textual, molecular SMILES strings, or combinations of formats such as scene graphs with text instructions.
  • Domain Applications: Table 1 summarizes task applications and exemplary queries for GraphRAG across different domains.
  • Query Processing: Figure 4 presents existing techniques for the GraphRAG query processor ΩProcessor.
  • Query Processing: Table 2 compares the query processor ΩProcessor in conventional RAG and GraphRAG.

2.3 Query Processor ΩProcessor

GraphRAG query processors bridge natural-language queries and graph-structured sources by identifying entities and relations, structuring or decomposing queries, and expanding them with graph context. These operations guide graph search for complex, multi-step tasks.

  • Name Entity Recognition: Entity recognition grounds query mentions to graph nodes that initialize retrieval and can also identify node types for subsequent exploration.Accurate recognition of entity names and structures reduces cascading errors in later retrieval and generation steps.
  • Relational Extraction: Relational extraction constructs graph data by extracting triplets and matches query relations to graph edges to guide neighborhood selection and traversal.For example, identifying “capital of” in a question supports searching corresponding knowledge-graph edges.
  • Query Structuration: Query structuration converts natural-language queries into source- and task-specific formats such as SQL, SPARQL, or graph query languages.The reviewed graph query languages include Cypher, GraphQL, and SPARQL.
  • Query Decomposition: Query decomposition splits complex inputs into distinct subqueries whose retrieved sub-results are aggregated into final results.Graph-structured subqueries can represent explicit logic connections and guide retrievers and generators through multistep prompting.
  • Query Expansion: Query expansion enriches underspecified or ambiguous queries, while GraphRAG variants additionally use structured relations such as neighboring nodes and template-based subqueries.These approaches extend beyond textual similarity by incorporating graph structure into expansion.

2.4 Retriever ΩRetriever

The GraphRAG retriever identifies relevant content from external graph sources, using heuristic, embedding-based, and advanced strategies suited to graph structure and complex queries.

  • Retriever role: The retriever ΩRetriever identifies relevant content C from external graph sources G after query processing.Retrieved content augments downstream task execution.
  • Heuristic-based retrievers: Heuristic retrievers use rules and graph algorithms such as entity linking, relational matching, traversal, and graph kernels.Entity linking maps query entities to graph nodes, while relational matching identifies query-aligned edges for traversal.
  • Heuristic-based retrievers: Heuristic methods are efficient and can distinguish subtly similar entities or relations, but fixed rules limit generalization to semantic or structural variations.Examples include distinguishing Byte from Bit and President of from Resident of, while confusing doctor with physician.
  • Learning-based retrievers: GraphRAG retrievers embed nodes, edges, and subgraphs using text, graph-based, or integrated text-and-graph encoders.Graph-based encoders specifically extend retrieval beyond i.i.d. data.
  • Learning-based retrievers: Shallow embeddings preserve structural information but struggle with semantic features and require retraining when graph content changes.These limitations reduce applicability to dynamically evolving GraphRAG sources.
  • Advanced retrieval strategies: Complex queries involving multiple aspects, structure patterns, and multi-hop reasoning motivate advanced retrieval strategies beyond basic retrievers.Examples require location-based entity identification, community-level aggregation, or resolving what “impactful” means.

2.5 Organizer

The organizer refines retrieved graph content before generation, addressing graph noise, context dilution, incompleteness, and the need to convert structured information into usable language.

  • Organizer role: The organizer ΩOrganizer processes retrieved entities, relations, triplets, paths, or subgraphs together with the processed query.Its purpose is to refine content for generator consumption.
  • Pruning: Graph pruning removes task-irrelevant nodes and edges from heterogeneous retrieved subgraphs that may be difficult for LLMs to digest.Semantic pruning methods score graph elements against the query and remove low-relevance nodes or clusters.
  • Reranking: Reranking prioritizes important retrieved content because larger traversal neighborhoods increase context length and can dilute the LLM’s focus.Methods rerank triples, paths, or passages using cross-encoders, rerankers, GNNs, or temporal ordering.
  • Design motivation: Organizer design must balance retrieval breadth and precision because too many hops introduce noise, while too few can leave graph content incomplete.This trade-off motivates pruning, reranking, augmentation, and verbalization techniques.
  • Augmentation: Graph augmentation adds supplementary nodes, edges, or information to enrich retrieved graphs or improve generator robustness.Methods may add noun-phrase nodes, connect the query as a node, or randomly drop graph elements for robustness.
  • Verbalization: Verbalization converts retrieved triples, paths, or graphs into natural language through linear rules or model-based generation.Linear methods include tuple- and template-based approaches, while model-based methods generate coherent graph-to-text descriptions.

2.6 Generator

GraphRAG generators produce task-specific outputs from queries and retrieved information, using graph models, LLM integration, or specialized scientific graph-generation architectures.

  • Generator types: Generators produce outputs for discrimination, generation, and graph-generation tasks based on the query and retrieved information.Examples include node, edge, or graph classification; knowledge-graph question answering; and molecular generation.
  • Discriminative generators: Discriminative generators use GNNs and Graph Transformers for graph classification and regression tasks.Model choice depends on graph type and task, with examples including GCN, GraphSAGE, GAT, and RGCN.
  • LLM integration: Verbalizing converts retrieved graph information into sequences that LLMs can process.The transformation provides language-compatible input but is part of the organizer pipeline.
  • LLM integration: Embedding-fusion aligns graph and text embeddings through a learned domain projector within LLM layers.Projected graph embeddings can be supplied through LLM self-attention layers.
  • LLM integration: Positional embedding-fusion adds graph-node positions to LLM inputs to retain structural information lost through direct verbalization.GIMLET uses generalized position embeddings for graph structures and textual instructions as unified tokens.
  • Scientific graph generation: Scientific GraphRAG generators often use transformer, Graph VAE, or diffusion architectures because accurate structure generation is required.RetMol supports multiple encoder, decoder, generative-model, and molecular-representation choices.

2.7 Graph Datasources

GraphRAG performance depends on carefully curated graph data sources, which can be constructed from explicit or implicit relationships and represented in task-appropriate formats.

  • Data-source construction: Graph data sources require careful curation because component configurations alone may not yield optimal GraphRAG performance.The survey therefore adopts a data-centric view of graph-source construction.
  • Construction methods: Explicit construction builds graphs from predefined relationships such as atom connections, entity links, citations, or user-item interactions.These relationships are directly present in the underlying data.
  • Construction methods: Implicit construction derives graph connections from signals such as word co-occurrence or feature interaction when explicit relationships are absent.The resulting graph can model shared semantic information or feature correlations.
  • Graph representations: Common graph representations include adjacency matrices, edge lists, and adjacency lists.Edge lists encode edges as pairs or relation-bearing triples such as (i, r, j).
  • Graph representations: Multi-relational attributed graphs require expanded representations that index node pairs by relation type.An adjacency tensor can be represented as A ∈ R^|V|×|V|×|R|, with A_i,j,r denoting relation-specific edge weight.
  • Graph representations: The order of graph descriptions significantly affects LLM comprehension and task performance.This finding makes representation choice relevant to downstream GraphRAG behavior.

3 Knowledge Graph

Knowledge graphs connect entities through defined relationships and support GraphRAG across tasks including question answering, fact-checking, completion, and cybersecurity. Their construction and retrieval involve multiple alternatives, from manual or rule-based graph creation to seed-entity identification and path or subgraph extraction.

  • Knowledge graphs connect entities through well-defined relationships and represent information as entities, relationships, paths, and subgraphs.
  • Applications: GraphRAG uses knowledge graphs for question answering, fact-checking, knowledge graph completion, and cybersecurity analysis.
  • Knowledge Graph Construction: Knowledge graph construction may be manual, rule-based, or LLM-based, with each approach organizing information differently.
  • Retriever: Retrieval begins by identifying seed entities that are highly relevant to the query and can anchor nearby graph context.
  • Retriever: GraphRAG retrieval methods include traversal-based, subgraph-based, and rule-based approaches for extracting relevant facts or entities.

4 Document Graph

Document graphs model relationships among documents and their internal components, supporting tasks that require global context or cross-document connections. They can be constructed explicitly from existing links or implicitly from semantic, syntactic, and structural relationships.

  • Document graphs represent connections among documents or document units, including hyperlinks, citations, and sentence–entity relationships.
  • Applications: Document graphs support multi-document summarization, text generation, document retrieval, classification, question answering, and relation extraction.
  • Applications: Document-level relationships help retrieval and reasoning when relevant terms are distributed across documents or questions require multi-hop understanding.
  • Graph Construction: Document graphs may be document-level, sentence-level, or word-level, so graph-construction methods are crucial for different tasks.
  • Graph Construction: Explicit construction uses existing links such as hyperlinks, citations, reposts, comments, and interactions between documents.
  • Graph Construction: Implicit construction captures internal semantic or syntactic relationships, including word, sentence, and document connections.

5 Scientific Graph

Scientific GraphRAG applies graph-structured molecular and biomedical knowledge to scientific generation and question answering. It addresses domain-specific validity challenges by retrieving relevant scientific graphs and uses multiple representations, construction methods, and retriever types.

  • Scientific graphs in this survey refer specifically to molecular graphs and medical graphs used in drug discovery and biomedicine.
  • Motivation: Scientific GraphRAG addresses incorrect structures, hallucinations, limited interpretability, and missing domain-specific expertise in scientific generation and medical question answering.
  • Motivation: Retrieving relevant scientific graphs guides generation or answering, incorporates known valid structures, and narrows the search space.
  • Applications: Molecule generation uses retrieved molecular structures to improve the rationality and accuracy of newly generated molecular structures.
  • Data Sources: GraphRAG data sources include public molecular databases such as PubChem, ChEMBL, and ZINC, alongside biomedical sources including PubMed and ClinicalTrials.
  • Representations: Molecules can be represented as 1D SMILES, 2D graphs, 3D graphs with coordinates, or text captions.
  • Construction and Retrieval: Scientific graph construction includes text-based, SMILES-based, and 3D graph methods, while retrieval includes heuristic-based and deep learning-based approaches.

6 Social Graph

Social graphs connect entities through interactions and support GraphRAG tasks such as prediction, generation, recommendation, question answering, and fake-news detection. Retrieval can exploit proximity, role, personalization, identifiers, filtering, social relations, and combined neural-symbolic methods.

  • Social graphs represent entities connected by social relations such as friendships, follows, likes, mentions, and other interactions.
  • Applications: Social GraphRAG supports entity property prediction, text generation, recommendation, question answering, and fake-news detection.
  • Retrieval Rationales: Social GraphRAG uses proximity-based, role-based, and personalization-based rationales to derive additional information from social graphs.
  • Graph Types: Social graphs include user-user, user-item, item-item, metadata, and agent-agent interaction types.
  • Graph Types: User-user, user-item, and metadata relations arise naturally, whereas item-item relations use manual extraction and agent-agent relations require simulations.
  • Retrieval Methods: Retrievers for social graphs include ID-based, filtering-based, social relational, and integrated neural-symbolic approaches.

7 Planning and Reasoning Graph

Planning and reasoning GraphRAG represents plans, tools, and reasoning steps as relational graphs, then retrieves relevant subgraphs through graph traversal. The surveyed literature covers diverse tasks, dependency-based construction, retrieval strategies, and generation patterns.

  • Graph types: Planning and reasoning graphs represent planning actions or reasoning steps as nodes connected by logical dependencies or reasoning flow.API tools can form planning graphs, while chain, tree, or graph-of-thought approaches represent decision-making steps.
  • Tasks: Representative tasks include sequential plan retrieval, asynchronous planning, structured commonsense reasoning, defeasible inference, tool usage, and embodied planning.Tool-use methods use A* search or Monte Carlo Tree Search to adaptively retrieve the next tool using internal evaluations and environmental feedback.
  • Graph construction: Graph construction commonly adds edges according to resource, temporal, or inclusive dependencies, although resource and causal relations are not equivalent.Resource dependency connects actions when one output matches another input; temporal dependency captures event order, while inclusive dependency captures shared categories or environments.
  • Retrieval: Retrievers typically seed graph traversal from the query and select relevant neighbors using embedding-based or heuristic-based criteria.Traversal expands until a budget or stopping criterion is met, with neighborhood selection determining the retriever category.
  • Organization: Planning and reasoning GraphRAG generally omits organizer mechanisms because multi-round similarity retrieval and reward-based search can provide sufficient precision.These retrieval strategies support plan fidelity and reduce the need for fine-grained reranking or filtering.
  • Generation: Most methods either output the retrieved plan directly or provide it to an LLM for downstream solution generation.Examples include returning graph-structured plans, compiling expert-tool results, and prompting an LLM to generate tool parameters.

8 Tabular Graph

Tabular GraphRAG models rows, features, cells, and cross-table relationships as graph structures for retrieval and prediction. The surveyed work spans node-, link-, graph-, table-question-answering, and table-retrieval tasks, but graph modeling remains early-stage.

  • Motivation: Serializing tables into text can lose structural information, motivating graph-based methods for tabular data with relational structure.Tabular data may contain one table or multiple tables linked by primary and foreign keys.
  • Tasks: Tabular graph tasks include node-level prediction, link-level prediction, graph-level prediction, table question answering, and table retrieval.Examples include fraud detection, data imputation, table similarity prediction, and answering queries over table content and relationships.
  • Graph structures: Tabular graphs model high-order feature and instance interactions using instance nodes, feature nodes, and heterogeneous instance-feature connections.Common structures include instance graphs, feature graphs, instance-feature graphs, and cell graphs.
  • Graph construction: Instance graphs are constructed with rule-based or similarity-based connections, or with learnable edge-weight refinement after heuristic initialization.Examples include expert rules, K-Nearest Neighbors, similarity thresholds, and dynamically adjusted edge weights.
  • Generation: Existing tabular GraphRAG methods commonly use GNNs or Graph Transformers as generators, sometimes combined with tabular predictors or LLM-based processing.Reported combinations include GNNs with DeepFM, FT-Transformer, XGBoost, AutoGluon, or gradient-boosted decision trees.
  • Resources and tools: RelBench and Tabular-benchmark provide resources for relational and tabular machine-learning evaluation, while listed toolkits support preprocessing, modeling, and evaluation.The surveyed tools include PyTorch Tabular, DeepTables, and PyTorch Frame.

9 Other Domains

GraphRAG research remains limited in infrastructure, biology, and scene domains, so the survey consolidates representative graph constructions, tasks, and methods. These domains use graphs to encode physical, biological, spatial, and semantic relationships.

  • Infrastructure: Infrastructure graphs represent Points of Presence connected by physical links and support applications across power, water, gas, transportation, and communication.Because GraphRAG research in infrastructure graphs is scarce, the review emphasizes graph construction and tasks with only a brief RAG overview.
  • Infrastructure: Infrastructure-network tasks include utility prediction, flow simulation and generation, vulnerability analysis, and network maintenance and operation.These tasks require understanding physical relationships for service delivery, forecasting, and network optimization.
  • Biology: Single-cell graphs derive gene and cell features from sequencing data and commonly construct cell relationships with K-Nearest Neighbors after normalization and dimensionality reduction.The survey also covers multi-omics and spatial-transcriptomics graphs that combine molecular modalities or retain spatial coordinates and images.
  • Scene graphs: Scene graphs encode spatial and semantic relationships among objects, attributes, and interactions within images.SceneGraphs provides 100,000 scene graphs for visual question answering and related tasks.
  • Scene GraphRAG: G-Retriever indexes scene-graph objects and attributes, retrieves relevant nodes and edges, constructs a filtered subgraph, and uses it with an LLM to generate answers.P-RAG instead builds and retrieves historical trajectory context from scene graphs generated from agent observations.
  • Other graph settings: Random graphs can be used to analyze GraphRAG retrieval, organization, and generation processes across complex-network settings.They are constructed probabilistically to produce diverse possible network structures.

10 Challenges and Future Work

The survey identifies open challenges across graph construction, retrieval, organization, generation, system integration, scalability, trustworthiness, and evaluation. Key issues include selecting suitable representations, preserving structure across modalities, reconciling knowledge, and benchmarking systems across tasks and domains.

  • Graph construction: Graph construction must address task- and domain-dependent choices about node and edge granularity, entity extraction, disambiguation, alignment, and coreference.It is also complex to determine whether one or multiple graphs are appropriate for an application.
  • Graph construction: Graph representations may not be equivalent, so selecting an effective format for a given task can significantly influence performance.The survey treats representation choice as an open graph-construction question.
  • Graph construction: Constructing cohesive multimodal graphs requires integrating text, images, audio, or video while preserving meaningful relationships.The challenge concerns maintaining relational structure across diverse data types.
  • Graph construction: Dynamic graphs require efficient strategies for construction, updating, and storage as real-world data evolve over time.These strategies must maintain both efficiency and effectiveness for downstream tasks.
  • Retriever: Retrievers must distinguish symbolic relations from neural-formatted knowledge and reconcile overlapping or conflicting internal and external knowledge.The survey also identifies a trade-off among retrieval accuracy, diversity, and novelty.
  • Organizer: Organizers must balance completeness against overwhelming the model by pruning irrelevant nodes and edges while retaining essential context.They also face challenges in structuring retrieved graphs, aligning heterogeneous resources, and augmenting data without irrelevant or redundant information.
  • Generator: Generators must convert retrieved texts, triplets, or graphs into effective prompts, while structural encoding remains insufficiently studied for whether LLMs recognize geometric structures.Current LLMs process text inputs, creating a format mismatch for structured retrieval.
  • System design: End-to-end GraphRAG systems must coordinate components, scale to larger graphs and query loads, and satisfy reliability, robustness, fairness, privacy, and explainability goals.GraphRAG can introduce additional privacy risks because relational connections may reveal sensitive information even when a sensitive node is protected.

11 Conclusion

The survey unifies GraphRAG architectures into a five-component framework, reviews representative techniques and domain-specific designs, and discusses challenges and opportunities.

  • The survey organizes existing GraphRAG approaches around graph construction, retriever, organizer, generator, and data source.
  • It reviews representative techniques for each GraphRAG component.
  • The survey examines GraphRAG designs tailored to diverse graph structures and application domains.
  • Applications across knowledge, document, scientific, and social graphs illustrate GraphRAG’s flexibility across tasks.
  • The survey discusses challenges and opportunities for advancing GraphRAG.
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