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The core of LLM4graph lies in transforming graphs into texts for LLMs to understand and analyze. Thus, we propose a novel taxonomy of LLM4graph methods from the view of the transformation. Specifically, existing methods can be divided into two paradigms: Graph2text and Graph2token, which transform graphs into texts or tokens as the input of LLMs, respectively. We point out four challenges during the transformation to systematically present existing methods from a problem-oriented perspective. For practical concerns, we provide a guideline for researchers on selecting appropriate models and LLMs for different graphs and hardware constraints. To empirically evaluate our taxonomy and different technical choices, we conduct experiments with representative methods in Graph2text and Graph2token. We also identify five future research directions for LLM4graph.<\/jats:p>","DOI":"10.1145\/3786600","type":"journal-article","created":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T13:45:46Z","timestamp":1767015946000},"page":"1-49","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Graph2text or Graph2token: A Perspective of Large Language Models for Graph Learning"],"prefix":"10.1145","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1124-9509","authenticated-orcid":false,"given":"Shuo","family":"Yu","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Dalian University of Technology, Dalian, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3807-7367","authenticated-orcid":false,"given":"Yingbo","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Software, Dalian University of Technology, Dalian, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-7791-1521","authenticated-orcid":false,"given":"Ruolin","family":"Li","sequence":"additional","affiliation":[{"name":"School of Software, Dalian University of Technology, Dalian, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-6485-3397","authenticated-orcid":false,"given":"Guchun","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Dalian University of Technology, Dalian, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4108-0230","authenticated-orcid":false,"given":"Yanming","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Dalian University of Technology, Dalian, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3281-8002","authenticated-orcid":false,"given":"Shaoxiong","family":"Ji","sequence":"additional","affiliation":[{"name":"ELLIS Institute Finland, Espoo, Finland and Department of Computing, University of Turku, Turku, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-6470-5607","authenticated-orcid":false,"given":"Bowen","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computing Technologies, RMIT University, Melbourne, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8756-7197","authenticated-orcid":false,"given":"Fengling","family":"Han","sequence":"additional","affiliation":[{"name":"School of Computing Technologies, RMIT University, Melbourne, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5558-3790","authenticated-orcid":false,"given":"Xiuzhen","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computing Technologies, RMIT University, Melbourne, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8324-1859","authenticated-orcid":false,"given":"Feng","family":"Xia","sequence":"additional","affiliation":[{"name":"School of Computing Technologies, RMIT University, Melbourne, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,2,16]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1561\/2000000137"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00778-019-00548-x"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/3696417"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbab340"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2021.103627"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW60847.2023.00184"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219890"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2013.2281156"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2015.2453957"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jnca.2017.12.004"},{"key":"e_1_3_2_12_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Kipf Thomas N.","year":"2017","unstructured":"Thomas N. 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Retrieved from https:\/\/arxiv.org\/abs\/2001.05140"},{"key":"e_1_3_2_115_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.findings-emnlp.132"},{"key":"e_1_3_2_116_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3149888"},{"key":"e_1_3_2_117_2","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671987"},{"key":"e_1_3_2_118_2","doi-asserted-by":"publisher","DOI":"10.1145\/3627673.3679830"},{"key":"e_1_3_2_119_2","doi-asserted-by":"publisher","DOI":"10.1145\/3701551.3703586"},{"key":"e_1_3_2_120_2","first-page":"12012","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Guo Zhichun","year":"2023","unstructured":"Zhichun Guo, William Shiao, Shichang Zhang, Yozen Liu, Nitesh V. Chawla, Neil Shah, and Tong Zhao. 2023. Linkless link prediction via relational distillation. 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Retrieved from https:\/\/arxiv.org\/abs\/2407.00696"},{"key":"e_1_3_2_137_2","doi-asserted-by":"publisher","DOI":"10.1145\/3627673.3679722"},{"key":"e_1_3_2_138_2","first-page":"1","volume-title":"Proceedings of Neural Information Processing Systems","author":"He Xiaoxin","year":"2024","unstructured":"Xiaoxin He, Yijun Tian, Yifei Sun, Nitesh V. Chawla, Thomas Laurent, Yann LeCun, Xavier Bresson, and Bryan Hooi. 2024. G-Retriever: Retrieval-augmented generation for textual graph understanding and question answering. In Proceedings of Neural Information Processing Systems, 1\u201332."},{"key":"e_1_3_2_139_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.108073"},{"key":"e_1_3_2_140_2","unstructured":"Bing Su Dazhao Du Zhao Yang Yujie Zhou Jiangmeng Li Anyi Rao Hao Sun Zhiwu Lu and Ji-Rong Wen. 2022. A molecular multimodal foundation model associating molecule graphs with natural language. arXiv:2209.05481. Retrieved from https:\/\/arxiv.org\/abs\/2209.05481"},{"key":"e_1_3_2_141_2","doi-asserted-by":"publisher","DOI":"10.1145\/3626772.3657693"},{"key":"e_1_3_2_142_2","doi-asserted-by":"publisher","DOI":"10.1145\/3706631"},{"key":"e_1_3_2_143_2","first-page":"354","volume-title":"Proceedings of the 31st International Conference on Computational Linguistics","author":"Cao He","year":"2025","unstructured":"He Cao, Zijing Liu, Xingyu Lu, Yuan Yao, and Yu Li. 2025. InstructMol: Multi-modal integration for building a versatile and reliable molecular assistant in drug discovery. In Proceedings of the 31st International Conference on Computational Linguistics, 354\u2013379."},{"key":"e_1_3_2_144_2","first-page":"1","volume-title":"Proceedings of the International Conference on Machine Learning 2024 Workshop on Efficient and Accessible Foundation Models for Biological Discovery","author":"Wang Runze","year":"2024","unstructured":"Runze Wang, Mingqi Yang, and Yanming Shen. 2024. Graph2Token: Make LLMs understand molecule graphs. In Proceedings of the International Conference on Machine Learning 2024 Workshop on Efficient and Accessible Foundation Models for Biological Discovery, 1\u20137."},{"key":"e_1_3_2_145_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.emnlp-main.966"},{"key":"e_1_3_2_146_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDS62089.2024.10756351"},{"key":"e_1_3_2_147_2","first-page":"1","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Zhao Qifang","year":"2025","unstructured":"Qifang Zhao, Weidong Ren, Tianyu Li, Xiaoxiao Xu, and Hong Liu. 2025. GraphGPT: Graph learning with generative pre-trained transformers. 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