@inproceedings{wang-etal-2025-demystifying,
title = "Demystifying the Power of Large Language Models in Graph Generation",
author = "Wang, Yu and
Rossi, Ryan A. and
Park, Namyong and
Ahmed, Nesreen K. and
Koutra, Danai and
Dernoncourt, Franck and
Derr, Tyler",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-naacl.456/",
doi = "10.18653/v1/2025.findings-naacl.456",
pages = "8174--8189",
ISBN = "979-8-89176-195-7",
abstract = "Despite the unprecedented success of applying Large Language Models (LLMs) to graph discriminative tasks such as node classification and link prediction, its potential for graph structure generation remains largely unexplored. To fill this crucial gap, this paper presents a systematic investigation into the capability of LLMs for graph structure generation. Specifically, we design prompts triggering LLMs to generate codes that optimize network properties by injecting domain expertise from network science. Since graphs in different domains exhibit unique structural properties captured by various metrics (e.g., clustering coefficient capturing triangles in social networks while squares reflecting road segments in transportation networks), we first evaluate the capability of LLMs to generate graphs satisfying each structural property in different domains. After that, we select the optimal property configurations and benchmark the graph structure generation performance of LLMs against established graph generative models across multiple domains. Our findings shed light on generating graph structures from an LLM perspective. Our code is publically available https://github.com/yuwvandy/LLM-GraphGen."
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<abstract>Despite the unprecedented success of applying Large Language Models (LLMs) to graph discriminative tasks such as node classification and link prediction, its potential for graph structure generation remains largely unexplored. To fill this crucial gap, this paper presents a systematic investigation into the capability of LLMs for graph structure generation. Specifically, we design prompts triggering LLMs to generate codes that optimize network properties by injecting domain expertise from network science. Since graphs in different domains exhibit unique structural properties captured by various metrics (e.g., clustering coefficient capturing triangles in social networks while squares reflecting road segments in transportation networks), we first evaluate the capability of LLMs to generate graphs satisfying each structural property in different domains. After that, we select the optimal property configurations and benchmark the graph structure generation performance of LLMs against established graph generative models across multiple domains. Our findings shed light on generating graph structures from an LLM perspective. Our code is publically available https://github.com/yuwvandy/LLM-GraphGen.</abstract>
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%0 Conference Proceedings
%T Demystifying the Power of Large Language Models in Graph Generation
%A Wang, Yu
%A Rossi, Ryan A.
%A Park, Namyong
%A Ahmed, Nesreen K.
%A Koutra, Danai
%A Dernoncourt, Franck
%A Derr, Tyler
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Findings of the Association for Computational Linguistics: NAACL 2025
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-195-7
%F wang-etal-2025-demystifying
%X Despite the unprecedented success of applying Large Language Models (LLMs) to graph discriminative tasks such as node classification and link prediction, its potential for graph structure generation remains largely unexplored. To fill this crucial gap, this paper presents a systematic investigation into the capability of LLMs for graph structure generation. Specifically, we design prompts triggering LLMs to generate codes that optimize network properties by injecting domain expertise from network science. Since graphs in different domains exhibit unique structural properties captured by various metrics (e.g., clustering coefficient capturing triangles in social networks while squares reflecting road segments in transportation networks), we first evaluate the capability of LLMs to generate graphs satisfying each structural property in different domains. After that, we select the optimal property configurations and benchmark the graph structure generation performance of LLMs against established graph generative models across multiple domains. Our findings shed light on generating graph structures from an LLM perspective. Our code is publically available https://github.com/yuwvandy/LLM-GraphGen.
%R 10.18653/v1/2025.findings-naacl.456
%U https://aclanthology.org/2025.findings-naacl.456/
%U https://doi.org/10.18653/v1/2025.findings-naacl.456
%P 8174-8189
Markdown (Informal)
[Demystifying the Power of Large Language Models in Graph Generation](https://aclanthology.org/2025.findings-naacl.456/) (Wang et al., Findings 2025)
ACL
- Yu Wang, Ryan A. Rossi, Namyong Park, Nesreen K. Ahmed, Danai Koutra, Franck Dernoncourt, and Tyler Derr. 2025. Demystifying the Power of Large Language Models in Graph Generation. In Findings of the Association for Computational Linguistics: NAACL 2025, pages 8174–8189, Albuquerque, New Mexico. Association for Computational Linguistics.