Exploring the Potential of Large Language Models (LLMs)in Learning on Graphs
Open accessAbstract
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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Only 6 of the 23 surveyed benchmark studies were peer-reviewed at the time of this article.
“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]
The HELM benchmark 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.
“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]
The HELM benchmark 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.
“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]
HELM acknowledged the challenge of accounting for linguistic diversity but did not provide a solution, focusing on English and limited consideration of other languages.
“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]
HELM acknowledged the challenge of ensuring uniform evaluation methods across all models, highlighting ongoing efforts to refine and adapt benchmarking practices, without explicitly stating that this inadequacy has been fully resolved.
“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]
HELM utilized a standardized few-shot prompting adaptation for all models, indicating awareness of prompt engineering challenges, but 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 biases or inaccuracies.
“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]
HELM implemented a comprehensive and systematic evaluation approach incorporating extensive metrics and scenarios, which inherently necessitated significant computational resources and time, likely extending iteration time; it did not specifically address or propose solutions to reduce slow test iteration time.
“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]
HELM incorporated metrics such as fairness, bias, and toxicity across diverse scenarios including multiple languages and cultural contexts, aiming to measure holistically, but acknowledged limitations in fully capturing pluralistic nature of human beliefs and values due to challenges in standardizing benchmarks.
“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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Self-citationQuantizing Text-attributed Graphs for Semantic-Structural Integration
Bo, J., Wu, H., & Fang, Y. (2025) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3711896.3737096 · Cited by 2 · see citing works
Graph Neural Networks Improve Quantized Transformers by Incorporating Global Relationships
Kumbasar, C. E., Özaktaş, H. M., & Koç, A. (2026) · IEEE Transactions on Signal and Information Processing over Networks · doi:10.1109/tsipn.2026.3654839 · Cited by 2 · see citing works
Knowledge Space reduction via Sequential Language Model Integration
Panagoulias, D. P., Virvou, M. K., & Tsihrintzis, G. A. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.09.544 · Cited by 2 · see citing works
A Student Emotions Recognition Based Online Texts with Large Language Models for Learning Prediction
Fan, G., Zhang, S., Cheng, S., & Yang, D. (2024) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm62325.2024.10822338 · Cited by 2 · see citing works
Deep Semantic Graph Learning via Large Language Models-Based Node Enhancement
Shi, C., Tao, Y., Zhang, H., Wang, L., Du, S., Shen, Y., & Shen, Y. (2025) · International Conference on Electrical Automation and Artificial Intelligence (ICEAAI) · doi:10.1109/iceaai64185.2025.10957582 · Cited by 2 · see citing works
Fine-tuned Vision Transformers and YOLOv11 for precise detection of pediatric Adenoid Hypertrophy
Sorour, S. E., Aljaafari, M. A. H., Alarfaj, A. A., Almusallam, W. H., & Aljoqiman, K. S. (2025) · Alexandria Engineering Journal · doi:10.1016/j.aej.2025.05.038 · Cited by 2 · see citing works
Large Language Models as a Service: Optimisation Strategies via Knowledge Space Reduction
Panagoulias, D. P., Virvou, M. K., & Tsihrintzis, G. A. (2024) · IEEE International Conference on Omni-layer Intelligent Systems (COINS) · doi:10.1109/coins61597.2024.10622117 · Cited by 2 · see citing works
Unraveling Tortured Phrases in the Scientific Literature through the Lens of TalbotsPreprint
Nguyen, T. H., Tran, T. Q., Hai, P. H., & Tan, N. H. (2023) · Research Square · doi:10.21203/rs.3.rs-3622785/v1 · Cited by 2 · see citing works
- 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 · 2023: 2
- 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 › Computer Vision and Pattern Recognition › 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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