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Exploring the Potential of Large Language Models (LLMs)in Learning on Graphs
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Learning on Graphs has attracted immense attention due to its wide real-world applications. The most popular pipeline for learning on graphs with textual node attributes primarily relies on Graph Neural Networks (GNNs), and utilizes shallow text embedding as initial node representations, which has limitations in general knowledge and profound semantic understanding. In recent years, Large Language Models (LLMs) have been proven to possess extensive common knowledge and powerful semantic comprehension abilities that have revolutionized existing workflows to handle text data. In this paper, we aim to explore the potential of LLMs in graph machine learning, especially the node classification task, and investigate two possible pipelines: LLMs-as-Enhancers and LLMs-as-Predictors. The former leverages LLMs to enhance nodes' text attributes with their massive knowledge and then generate predictions through GNNs. The latter attempts to directly employ LLMs as standalone predictors. We conduct comprehensive and systematical studies on these two pipelines under various settings. From comprehensive empirical results, we make original observations and find new insights that open new possibilities and suggest promising directions to leverage LLMs for learning on graphs. Our codes and datasets are available at: https://github.com/CurryTang/Graph-LLM .
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“Our review also found that only 6 of the 23 surveyed benchmark studies ( [7], [10], [31], [39], [44], [52]) were peerreviewed at the time of this article, reflecting the early-stage nature of research in this domain.”
From Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligence (2026) · cites this paper as [31]
In the citing paper
“HELM [31]: The study acknowledged the challenge of standardizing language model evaluation across diverse scenarios, indicating an understanding of response variability but not fully addressing it through its methodology.”
From Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligence (2026) · cites this paper as [31]
In the citing paper
“HELM [31]: The HELM evaluation framework included targeted evaluations and multi-metric measurement across various scenarios, but did not specifically address the tension between helpfulness and harmlessness in its criteria or methodologies, indicating an unresolved challenge in balancing these aspects within the”
From Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligence (2026) · cites this paper as [31]
In the citing paper
“The study acknowledged the challenge of accounting for linguistic diversity in benchmarks, but did not provide a solution, exemplified by its focus on English and limited consideration of other languages, which indicates an unaddressed and unresolved inadequacy regarding linguistic differences and embedded logics.”
From Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligence (2026) · cites this paper as [31]
In the citing paper
“The study acknowledged the challenge of ensuring uniform evaluation methods across all models, highlighting ongoing efforts to refine and adapt benchmarking practices to maintain relevancy and accuracy in assessments, without explicitly stating that this inadequacy has been fully resolved.”
From Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligence (2026) · cites this paper as [31]
In the citing paper
“The study utilized a standardized fewshot prompting adaptation for all models, indicating an awareness of prompt engineering challenges.However, it also highlighted the sensitivity of model performance to prompt formatting and adaptation methods, revealing an ongoing struggle with crafting prompts that accurately assess model capabilities without introducing biases or inaccuracies.”
From Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligence (2026) · cites this paper as [31]
In the citing paper
“The study implemented a comprehensive and systematic evaluation approach to benchmarking LLMs, incorporating extensive metrics and scenarios, which inherently necessitated significant computational resources and time for thorough evaluation, thus likely extending iteration time for model testing.The study, while acknowledging the broad and intricate scope of its benchmark, did not specifically”
From Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligence (2026) · cites this paper as [31]
In the citing paper
“The study's extensive evaluation framework incorporated metrics such as fairness, bias, and toxicity across a diverse set of scenarios, including multiple languages and cultural contexts, aiming to measure LLMs' performance holistically.However, the study acknowledged its limitations in fully capturing the pluralistic nature of human beliefs and values, due to inherent challenges in standardizing”
From Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligence (2026) · cites this paper as [31]
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Cited by
Sorted by citations
Recommender Systems in the Era of Large Language Models (LLMs)
Zhao, Z., Fan, W., Li, J., Liu, Y., Mei, X., Wang, Y., Wen, Z., Wang, F., Zhao, X., Tang, J., et al. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3392335 · Cited by 481
Self-citation(2023) · doi:10.18653/v1/2023.emnlp-main.574 · Cited by 249
Graph Retrieval-Augmented Generation: A Survey
Peng, B., Zhu, Y., Liu, Y., Bo, X., Shi, H., Hong, C., Zhang, Y., & Tang, S. (2026) · ACM Transactions on Information Systems · doi:10.1145/3777378 · Cited by 206
Dai, E., Zhao, T., Zhu, H., Xu, J., Guo, Z., Liu, H., Tang, J., & Wang, S. (2024) · Machine Intelligence Research · doi:10.1007/s11633-024-1510-8 · Cited by 257
Self-citationLarge Language Models on Graphs: A Comprehensive Survey
Jin, B., Liu, G., Han, C., Jiang, M., Ji, H., & Han, J. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3469578 · Cited by 278
Metadata
- Authors
- Chen, Z., Mao, H., Li, H., Jin, W., Wen, H., Wei, X., Wang, S., Yin, D., Fan, W., Liu, H., & Tang, J.
- Affiliations
- Michigan State University (US)
- Emory University (US)
- Baidu (China) (CN)
- Hong Kong Polytechnic University (HK)
- ORCID
- Venue
- ACM SIGKDD Explorations Newsletter
- Published
- 2024-03-26
- Volume
- 25
- Issue
- 2
- Pages
- 42-61
- ISSN
- 1931-0145 (ISSN-L)
- 1931-0153
- OpenAlex
- W4393277515
- Cited by
- 209
- References
- 57
- Citations by year
- 2026: 57 · 2025: 104 · 2024: 46
- FWCI
- 30.67
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM SIGKDD Explorations Newsletter (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Graph Theory and Algorithms
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- large language models, graph neural networks, node classification, text embeddings, semantic understanding, knowledge improvement
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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