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A Survey of Graph Meets Large Language Model: Progress and Future Directions

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arxiv 2311.12399 v4 pith:XHVYJVWT submitted 2023-11-21 cs.LG cs.CLcs.SI

Yuhan Li , Zhixun Li , Peisong Wang , Jia Li , Xiangguo Sun , Hong Cheng , Jeffrey Xu Yu This is my paper · ORCID
classification cs.LGcs.CLcs.SI
keywords methodsexistinggraphllmsnetworkssurveycategoriesfirst
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Graph plays a significant role in representing and analyzing complex relationships in real-world applications such as citation networks, social networks, and biological data. Recently, Large Language Models (LLMs), which have achieved tremendous success in various domains, have also been leveraged in graph-related tasks to surpass traditional Graph Neural Networks (GNNs) based methods and yield state-of-the-art performance. In this survey, we first present a comprehensive review and analysis of existing methods that integrate LLMs with graphs. First of all, we propose a new taxonomy, which organizes existing methods into three categories based on the role (i.e., enhancer, predictor, and alignment component) played by LLMs in graph-related tasks. Then we systematically survey the representative methods along the three categories of the taxonomy. Finally, we discuss the remaining limitations of existing studies and highlight promising avenues for future research. The relevant papers are summarized and will be consistently updated at: https://github.com/yhLeeee/Awesome-LLMs-in-Graph-tasks.

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Cited by 13 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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    Under a fixed leakage-free protocol, NC→LP transfer reliably helps on homophilic graphs while LP→NC helps mainly when LP is easy and NC is unsaturated; homophily and CoTask Score guide mechanism choice.

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    QUIET is a hierarchical RVQ-based graph tokenizer with a learned level-weighting gate; it improves several benchmarks but not consistently against the strongest baselines.

  3. Text-Attributed Graph Anomaly Detection via Multi-Scale Cross- and Uni-Modal Contrastive Learning

    cs.LG 2025-08 conditional novelty 6.0 of 10

    A joint text-graph contrastive learning framework detects anomalies in text-attributed graphs and outperforms eleven baselines across eight new datasets.

  4. EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora

    cs.IR 2025-06 conditional novelty 6.0 of 10

    EraRAG uses hyperplane-based locality-sensitive hashing to build a hierarchical retrieval graph whose affected regions only are re-summarized when new documents arrive, cutting update cost by up to an order of magnitude.

  5. GraphLAMA: Enabling Efficient Adaptation of Graph Language Models with Limited Annotations

    cs.CL 2025-06 conditional novelty 6.0 of 10

    A frozen-LLM graph model with a small tuned adapter (about 726k parameters, 3MB) outperforms in-context learning and instruction-tuned graph LLMs in few/zero-shot node classification and summary generation.

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    A query-conditioned temporal graph RAG with chain-of-trees fusion reaches 0.3738 Recall@5 on a Victorian crime diaries QA set, beating standard and graph RAG baselines.

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    cs.LG 2025-07 conditional novelty 5.0 of 10

    DGP condenses neighbors into coarse-grained summaries while preserving the target node's full text, improving LLM-based fraud detection by up to 6.8 absolute AUPRC points over state-of-the-art methods.

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    GraphLLMs are broadly vulnerable to text, graph structure, and prompt label attacks, but the severity depends heavily on the model and dataset.

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    In a small comparative study, GraphRAG outscored spaCy and CoreNLP-based KG-QA pipelines on reasoning-heavy questions, but the evaluation design conflates method choice with pipeline architecture.

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    In a small Earth-science reranking dataset, DPO-trained LLMs rank best and SHAP attribution scores help a general LLM explain why items were selected, but the explanation claim rests on only two examples.

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    A review that groups LLM-GNN trustworthiness research under four dimensions, reliability, robustness, privacy, and reasoning, with no new experiments.

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    A comprehensive survey that categorizes graph foundation model approaches to recommendation into graph-augmented LLM, LLM-augmented graph, and LLM-graph harmonization.

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