{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T17:46:34Z","timestamp":1778694394097,"version":"3.51.4"},"reference-count":276,"publisher":"Association for Computing Machinery (ACM)","issue":"5","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2025,10,31]]},"abstract":"<jats:p>Graphs play an important role in representing complex relationships in various domains like social networks, knowledge graphs, and molecular discovery. With the advent of deep learning, Graph Neural Networks (GNNs) have emerged as a cornerstone in Graph Machine Learning (Graph ML), facilitating the representation and processing of graphs. Recently, LLMs have demonstrated unprecedented capabilities in language tasks and are widely adopted in a variety of applications, such as computer vision and recommender systems. This remarkable success has also attracted interest in applying LLMs to the graph domain. Increasing efforts have been made to explore the potential of LLMs in advancing Graph ML\u2019s generalization, transferability, and few-shot learning ability. Meanwhile, graphs, especially knowledge graphs, are rich in reliable factual knowledge, which can be utilized to enhance the reasoning capabilities of LLMs and potentially alleviate their limitations, such as hallucinations and the lack of explainability. Given the rapid progress of this research direction, a systematic review summarizing the latest advancements for Graph ML in the era of LLMs is necessary to provide an in-depth understanding to researchers and practitioners. Therefore, in this survey, we first review the recent developments in Graph ML. We then explore how LLMs can be utilized to enhance the quality of graph features, alleviate the reliance on labeled data, and address challenges such as graph Heterophily and Out-of-Distribution (OOD) generalization. Afterward, we delve into how graphs can enhance LLMs, highlighting their abilities to enhance LLM pre-training and inference. Furthermore, we investigate various applications and discuss the potential future directions in this promising field.<\/jats:p>","DOI":"10.1145\/3732786","type":"journal-article","created":{"date-parts":[[2025,5,6]],"date-time":"2025-05-06T10:45:56Z","timestamp":1746528356000},"page":"1-40","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":15,"title":["Graph Machine Learning in the Era of Large Language Models (LLMs)"],"prefix":"10.1145","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7389-3810","authenticated-orcid":false,"given":"Shijie","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Computing, The Hong Kong Polytechnic University, Hong Kong, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7016-982X","authenticated-orcid":false,"given":"Jiani","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Computing, The Hong Kong Polytechnic University, Hong Kong, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-7305-8629","authenticated-orcid":false,"given":"Zhikai","family":"Chen","sequence":"additional","affiliation":[{"name":"Michigan State University, East Lansing, Michigan, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8940-2561","authenticated-orcid":false,"given":"Yu","family":"Song","sequence":"additional","affiliation":[{"name":"Michigan State University, East Lansing, Michigan, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7038-5765","authenticated-orcid":false,"given":"Wenzhuo","family":"Tang","sequence":"additional","affiliation":[{"name":"Statistics and Probability, Michigan State University, East Lansing, Michigan, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-8510-3102","authenticated-orcid":false,"given":"Haitao","family":"Mao","sequence":"additional","affiliation":[{"name":"Michigan State University, East Lansing, Michigan, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4049-1233","authenticated-orcid":false,"given":"Wenqi","family":"Fan","sequence":"additional","affiliation":[{"name":"The Hong Kong Polytechnic University, Hong Kong, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3555-3495","authenticated-orcid":false,"given":"Hui","family":"Liu","sequence":"additional","affiliation":[{"name":"Michigan State University, East Lansing, Michigan, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8217-5688","authenticated-orcid":false,"given":"Xiaorui","family":"Liu","sequence":"additional","affiliation":[{"name":"North Carolina State University, Raleigh, North Carolina, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0684-6205","authenticated-orcid":false,"given":"Dawei","family":"Yin","sequence":"additional","affiliation":[{"name":"Baidu Inc, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3370-471X","authenticated-orcid":false,"given":"Qing","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computing, The Hong Kong Polytechnic University, Hong Kong, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,8,18]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"Garima Agrawal Tharindu Kumarage Zeyad Alghami and Huan Liu. 2023. Can knowledge graphs reduce hallucinations in LLMs? A survey. arXiv:2311.07914. Retrieved from https:\/\/arxiv.org\/abs\/2311.07914"},{"key":"e_1_3_1_3_2","volume-title":"The 12th International Conference on Learning Representations","author":"Asai Akari","year":"2023","unstructured":"Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi. 2023. Self-rag: Learning to retrieve, generate, and critique through self-reflection. In The 12th International Conference on Learning Representations. Retrieved from https:\/\/openreview.net\/forum?id=hSyW5go0v8"},{"key":"e_1_3_1_4_2","doi-asserted-by":"crossref","unstructured":"Jinheon Baek Alham Fikri Aji and Amir Saffari. 2023. Knowledge-augmented language model prompting for zero-shot knowledge graph question answering. arXiv:2306.04136. Retrieved from https:\/\/arxiv.org\/abs\/2306.04136","DOI":"10.18653\/v1\/2023.matching-1.7"},{"key":"e_1_3_1_5_2","article-title":"Accurate learning of graph representations with graph multiset pooling","author":"Baek Jinheon","year":"2021","unstructured":"Jinheon Baek, Minki Kang, and Sung Ju Hwang. 2021. Accurate learning of graph representations with graph multiset pooling. In International Conference on Learning Representations. Retrieved from https:\/\/openreview.net\/forum?id=JHcqXGaqiGn","journal-title":"International Conference on Learning Representations"},{"key":"e_1_3_1_6_2","unstructured":"Suryanarayanan Balaji Rishikesh Magar Yayati Jadhav and Amir Barati Farimani. 2023. GPT-MolBERTa: GPT molecular features language model for molecular property prediction. arXiv:2310.03030. Retrieved from https:\/\/arxiv.org\/abs\/2310.03030"},{"key":"e_1_3_1_7_2","doi-asserted-by":"crossref","unstructured":"Keqin Bao Jizhi Zhang Yang Zhang Wenjie Wang Fuli Feng and Xiangnan He. 2023. TALLRec: An effective and efficient tuning framework to align large language model with recommendation. arXiv:2305.00447. Retrieved from https:\/\/arxiv.org\/abs\/2305.00447","DOI":"10.1145\/3604915.3608857"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.acl-industry.56"},{"key":"e_1_3_1_9_2","first-page":"2206","volume-title":"International Conference on Machine Learning","author":"Borgeaud Sebastian","year":"2022","unstructured":"Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai, Eliza Rutherford, Katie Millican, George Bm Van Den Driessche, Jean-Baptiste Lespiau, Bogdan Damoc, Aidan Clark, et al. 2022. Improving language models by retrieving from trillions of tokens. In International Conference on Machine Learning. PMLR, 2206\u20132240."},{"key":"e_1_3_1_10_2","unstructured":"Andres M. Bran Sam Cox Andrew D. White and Philippe Schwaller. 2023. ChemCrow: Augmenting large-language models with chemistry tools. arXiv:2304.05376. Retrieved from https:\/\/arxiv.org\/abs\/2304.05376"},{"key":"e_1_3_1_11_2","unstructured":"Ulrik Brandes Markus Eiglsperger J\u00fcrgen Lerner and Christian Pich. 2013. Graph markup language (GraphML). Retrieved from https:\/\/cs.brown.edu\/people\/rtamassi\/gdhandbook\/chapters\/graphml.pdf"},{"key":"e_1_3_1_12_2","first-page":"1877","article-title":"Language models are few-shot learners","volume":"33","author":"Brown Tom","year":"2020","unstructured":"Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D. Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020. Language models are few-shot learners. In Proceedings of the 34th International Conference on Neural Information Processing Systems Vol. 33, 1877\u20131901.","journal-title":"Proceedings of the 34th International Conference on Neural Information Processing Systems"},{"key":"e_1_3_1_13_2","unstructured":"S\u00e9bastien Bubeck Varun Chandrasekaran Ronen Eldan Johannes Gehrke Eric Horvitz Ece Kamar Peter Lee Yin Tat Lee Yuanzhi Li Scott Lundberg et al. 2023. Sparks of artificial general intelligence: Early experiments with gpt-4. arXiv:2303.12712. Retrieved from https:\/\/arxiv.org\/abs\/2303.12712"},{"key":"e_1_3_1_14_2","unstructured":"He Cao Zijing Liu Xingyu Lu Yuan Yao and Yu Li. 2023. Instructmol: Multi-modal integration for building a versatile and reliable molecular assistant in drug discovery. arXiv:2311.16208. Retrieved from https:\/\/arxiv.org\/abs\/2311.16208"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbac346"},{"key":"e_1_3_1_16_2","first-page":"2633","volume-title":"6th USENIX Security Symposium","author":"Carlini Nicholas","year":"2021","unstructured":"Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom B. Brown, Dawn Song, Ulfar Erlingsson, et al. 2021. Extracting training data from large language models. In 6th USENIX Security Symposium, 2633\u20132650."},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.3c00285"},{"key":"e_1_3_1_18_2","unstructured":"Ziwei Chai Tianjie Zhang Liang Wu Kaiqiao Han Xiaohai Hu Xuanwen Huang and Yang Yang. 2023. GraphLLM: Boosting graph reasoning ability of large language model. arXiv:2310.05845. Retrieved from https:\/\/arxiv.org\/abs\/2310.05845"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.3389\/frobt.2023.1221739"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539359"},{"key":"e_1_3_1_21_2","unstructured":"Mouxiang Chen Zemin Liu Chenghao Liu Jundong Li Qiheng Mao and Jianling Sun.2023. ULTRA-DP: Unifying graph pre-training with multi-task graph dual prompt. arXiv:2310.14845. Retrieved from https:\/\/arxiv.org\/abs\/2310.14845"},{"key":"e_1_3_1_22_2","doi-asserted-by":"crossref","unstructured":"Nuo Chen Yuhan Li Jianheng Tang and Jia Li. 2024. GraphWiz: An instruction-following language model for graph problems. arXiv:2402.16029. Retrieved from https:\/\/arxiv.org\/abs\/2402.16029","DOI":"10.1145\/3637528.3672010"},{"key":"e_1_3_1_23_2","unstructured":"Runjin Chen Tong Zhao Ajay Jaiswal Neil Shah and Zhangyang Wang. 2024. LLaGA: Large language and graph assistant. arXiv:2402.08170. Retrieved from https:\/\/arxiv.org\/abs\/2402.08170"},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583355"},{"key":"e_1_3_1_25_2","unstructured":"Yongqiang Chen Han Yang Yonggang Zhang Kaili Ma Tongliang Liu Bo Han and James Cheng. 2022. Understanding and improving graph injection attack by promoting unnoticeability. arXiv:2202.08057. Retrieved from https:\/\/arxiv.org\/abs\/2202.08057"},{"key":"e_1_3_1_26_2","unstructured":"Zichen Chen Jianda Chen Mitali Gaidhani Ambuj Singh and Misha Sra. 2023. XplainLLM: A QA explanation dataset for understanding LLM decision-making. arXiv:2311.08614. Retrieved from https:\/\/arxiv.org\/abs\/2311.08614"},{"key":"e_1_3_1_27_2","unstructured":"Zhikai Chen Haitao Mao Hang Li Wei Jin Hongzhi Wen Xiaochi Wei Shuaiqiang Wang Dawei Yin Wenqi Fan Hui Liu and Jiliang Tang. 2023. Exploring the potential of large language models (LLMs) in learning on graphs. arXiv:2307.03393. Retrieved from https:\/\/arxiv.org\/abs\/2307.03393"},{"key":"e_1_3_1_28_2","unstructured":"Wei-Lin Chiang Zhuohan Li Zi Lin Ying Sheng Zhanghao Wu Hao Zhang Lianmin Zheng Siyuan Zhuang Yonghao Zhuang Joseph E. Gonzalez et al. 2023. Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality. Retrieved April 14 2023 from https:\/\/vicuna.lmsys.org"},{"key":"e_1_3_1_29_2","unstructured":"Eli Chien Wei-Cheng Chang Cho-Jui Hsieh Hsiang-Fu Yu Jiong Zhang Olgica Milenkovic and Inderjit S. Dhillon. 2021. Node feature extraction by self-supervised multi-scale neighborhood prediction. arXiv:2111.00064. Retrieved from https:\/\/arxiv.org\/abs\/2111.00064"},{"key":"e_1_3_1_30_2","unstructured":"Aakanksha Chowdhery Sharan Narang Jacob Devlin Maarten Bosma Gaurav Mishra Adam Roberts Paul Barham Hyung Won Chung Charles Sutton Sebastian Gehrmann et al. 2022. Palm: Scaling language modeling with pathways. arXiv:2204.02311. Retrieved from https:\/\/arxiv.org\/abs\/2204.02311"},{"key":"e_1_3_1_31_2","first-page":"1115","volume-title":"International Conference on Machine Learning","author":"Dai Hanjun","year":"2018","unstructured":"Hanjun Dai, Hui Li, Tian Tian, Xin Huang, Lin Wang, Jun Zhu, and Le Song. 2018. Adversarial attack on graph structured data. In International Conference on Machine Learning. PMLR, 1115\u20131124."},{"key":"e_1_3_1_32_2","unstructured":"Sunhao Dai Ninglu Shao Haiyuan Zhao Weijie Yu Zihua Si Chen Xu Zhongxiang Sun Xiao Zhang and Jun Xu. 2023. Uncovering ChatGPT\u2019s capabilities in recommender systems. arXiv:2305.02182. Retrieved from https:\/\/arxiv.org\/abs\/2305.02182"},{"key":"e_1_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371807"},{"key":"e_1_3_1_34_2","unstructured":"Tim Dettmers Artidoro Pagnoni Ari Holtzman and Luke Zettlemoyer. 2023. Qlora: Efficient finetuning of quantized llms. arXiv:2305.14314. Retrieved from https:\/\/arxiv.org\/abs\/2305.14314"},{"key":"e_1_3_1_35_2","unstructured":"Jacob Devlin Ming-Wei Chang Kenton Lee and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805. Retrieved from https:\/\/arxiv.org\/abs\/1810.04805"},{"key":"e_1_3_1_36_2","unstructured":"Keyu Duan Qian Liu Tat-Seng Chua Shuicheng Yan Wei Tsang Ooi Qizhe Xie and Junxian He. 2023. Simteg: A frustratingly simple approach improves textual graph learning. arXiv:2308.02565. Retrieved from https:\/\/arxiv.org\/abs\/2308.02565"},{"key":"e_1_3_1_37_2","unstructured":"Vijay Prakash Dwivedi and Xavier Bresson. 2020. A generalization of transformer networks to graphs. arXiv:2012.09699. Retrieved from https:\/\/arxiv.org\/abs\/2012.09699"},{"key":"e_1_3_1_38_2","doi-asserted-by":"publisher","DOI":"10.5555\/3367032.3367224"},{"key":"e_1_3_1_39_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE51399.2021.00140"},{"key":"e_1_3_1_40_2","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671470"},{"key":"e_1_3_1_41_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE55515.2023.00056"},{"key":"e_1_3_1_42_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.12132"},{"key":"e_1_3_1_43_2","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313488"},{"key":"e_1_3_1_44_2","first-page":"2033","article-title":"A graph neural network framework for social recommendations","author":"Fan Wenqi","year":"2020","unstructured":"Wenqi Fan, Yao Ma, Qing Li, Jianping Wang, Guoyong Cai, Jiliang Tang, and Dawei Yin. 2020. A graph neural network framework for social recommendations. IEEE Transactions on Knowledge and Data Engineering 34 (2020), 2033\u20132047.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_1_45_2","doi-asserted-by":"publisher","DOI":"10.1145\/3298689.3347011"},{"key":"e_1_3_1_46_2","unstructured":"Wenqi Fan Xiangyu Zhao Xiao Chen Jingran Su Jingtong Gao Lin Wang Qidong Liu Yiqi Wang Han Xu Lei Chen et al. 2022. A comprehensive survey on trustworthy recommender systems. arXiv:2209.10117. Retrieved from https:\/\/arxiv.org\/abs\/2209.10117"},{"key":"e_1_3_1_47_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2023.3272652"},{"key":"e_1_3_1_48_2","unstructured":"Wenqi Fan Zihuai Zhao Jiatong Li Yunqing Liu Xiaowei Mei Yiqi Wang Zhen Wen Fei Wang Xiangyu Zhao Jiliang Tang and Qing Li. 2023. Recommender systems in the era of large language models (LLMs). arXiv:2307.02046. Retrieved from https:\/\/arxiv.org\/abs\/2307.02046"},{"key":"e_1_3_1_49_2","unstructured":"Wenqi Fan Yi Zhou Shijie Wang Yuyao Yan Hui Liu Qian Zhao Le Song and Qing Li. 2025. Computational protein science in the era of large language models (LLMs). arXiv:2501.10282. Retrieved from https:\/\/arxiv.org\/abs\/2501.10282"},{"key":"e_1_3_1_50_2","first-page":"5534","article-title":"A knowledge-enriched ensemble method for word embedding and multi-sense embedding","author":"Fang Lanting","year":"2022","unstructured":"Lanting Fang, Yong Luo, Kaiyu Feng, Kaiqi Zhao, and Aiqun Hu. 2022. A knowledge-enriched ensemble method for word embedding and multi-sense embedding. IEEE Transactions on Knowledge and Data Engineering 35, 6 (2022), 5534\u20135549.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_1_51_2","first-page":"52464","volume-title":"37th Conference on Neural Information Processing Systems","volume":"36","author":"Fang Taoran","year":"2023","unstructured":"Taoran Fang, Yunchao Mercer Zhang, Yang Yang, Chunping Wang, and Chen Lei. 2023. Universal prompt tuning for graph neural networks. In 37th Conference on Neural Information Processing Systems, Vol. 36, 52464\u201352489."},{"key":"e_1_3_1_52_2","unstructured":"Bahare Fatemi Jonathan Halcrow and Bryan Perozzi. 2023. Talk like a graph: Encoding graphs for large language models. arXiv:2310.04560. Retrieved from https:\/\/arxiv.org\/abs\/2310.04560"},{"key":"e_1_3_1_53_2","unstructured":"Chao Feng Xinyu Zhang and Zichu Fei. 2023. Knowledge solver: Teaching LLMs to search for domain knowledge from knowledge graphs. arXiv:2309.03118. Retrieved from https:\/\/arxiv.org\/abs\/2309.03118"},{"key":"e_1_3_1_54_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.robot.2008.08.007"},{"key":"e_1_3_1_55_2","unstructured":"Mikhail Galkin Xinyu Yuan Hesham Mostafa Jian Tang and Zhaocheng Zhu. 2023. Towards foundation models for knowledge graph reasoning. arXiv:2310.04562. Retrieved from https:\/\/arxiv.org\/abs\/2310.04562"},{"key":"e_1_3_1_56_2","doi-asserted-by":"publisher","DOI":"10.1145\/3488560.3501396"},{"key":"e_1_3_1_57_2","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbab159"},{"key":"e_1_3_1_58_2","unstructured":"Qingqing Ge Zeyuan Zhao Yiding Liu Anfeng Cheng Xiang Li Shuaiqiang Wang and Dawei Yin. 2023. Enhancing graph neural networks with structure-based prompt. arXiv:2310.17394. Retrieved from https:\/\/arxiv.org\/abs\/2310.17394"},{"key":"e_1_3_1_59_2","first-page":"7637","article-title":"Robustness of graph neural networks at scale","volume":"34","author":"Geisler Simon","year":"2021","unstructured":"Simon Geisler, Tobias Schmidt, Hakan \u015eirin, Daniel Z\u00fcgner, Aleksandar Bojchevski, and Stephan G\u00fcnnemann. 2021. Robustness of graph neural networks at scale. Advances in Neural Information Processing Systems 34 (2021), 7637\u20137649.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_60_2","doi-asserted-by":"publisher","DOI":"10.1145\/3523227.3546767"},{"key":"e_1_3_1_61_2","first-page":"1263","volume-title":"International Conference on Machine Learning","author":"Gilmer Justin","year":"2017","unstructured":"Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl. 2017. Neural message passing for quantum chemistry. In International Conference on Machine Learning. PMLR, 1263\u20131272."},{"key":"e_1_3_1_62_2","doi-asserted-by":"publisher","DOI":"10.2196\/45312"},{"key":"e_1_3_1_63_2","unstructured":"Chenghua Gong Xiang Li Jianxiang Yu Cheng Yao Jiaqi Tan Chengcheng Yu and Dawei Yin. 2023. Prompt tuning for multi-view graph contrastive learning. arXiv:2310.10362. Retrieved from https:\/\/arxiv.org\/abs\/2310.10362"},{"key":"e_1_3_1_64_2","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939754"},{"key":"e_1_3_1_65_2","first-page":"1","article-title":"Large language models for link stealing attacks against graph neural networks","author":"Guan Faqian","year":"2024","unstructured":"Faqian Guan, Tianqing Zhu, Hui Sun, Wanlei Zhou, and S. Yu Philip. 2024. Large language models for link stealing attacks against graph neural networks. IEEE Transactions on Big Data (2024), 1\u201315. Retrieved from https:\/\/ieeexplore.ieee.org\/abstract\/document\/10747296","journal-title":"IEEE Transactions on Big Data"},{"key":"e_1_3_1_66_2","unstructured":"Xinyan Guan Yanjiang Liu Hongyu Lin Yaojie Lu Ben He Xianpei Han and Le Sun. 2023. Mitigating large language model hallucinations via autonomous knowledge graph-based retrofitting. arXiv:2311.13314. Retrieved from https:\/\/arxiv.org\/abs\/2311.13314"},{"key":"e_1_3_1_67_2","unstructured":"Anchun Gui Jinqiang Ye and Han Xiao. 2023. G-Adapter: Towards structure-aware parameter-efficient transfer learning for graph transformer networks. arXiv:2305.10329. Retrieved from https:\/\/arxiv.org\/abs\/2305.10329"},{"key":"e_1_3_1_68_2","first-page":"2059","article-title":"Good: A graph out-of-distribution benchmark","volume":"35","author":"Gui Shurui","year":"2022","unstructured":"Shurui Gui, Xiner Li, Limei Wang, and Shuiwang Ji. 2022. Good: A graph out-of-distribution benchmark. Advances in Neural Information Processing Systems 35 (2022), 2059\u20132073.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_69_2","doi-asserted-by":"publisher","unstructured":"Han Guo Mingjia Huo Ruiyi Zhang and Pengtao Xie. 2023. ProteinChat: Towards achieving ChatGPT-like functionalities on protein 3D structures. Retrieved from 10.36227\/techrxiv.23120606.v1","DOI":"10.36227\/techrxiv.23120606.v1"},{"key":"e_1_3_1_70_2","unstructured":"Jiayan Guo Lun Du Hengyu Liu Mengyu Zhou Xinyi He and Shi Han. 2023. GPT4Graph: Can large language models understand graph structured data? An empirical evaluation and benchmarking. arXiv:2305.15066. Retrieved from https:\/\/arxiv.org\/abs\/2305.15066"},{"key":"e_1_3_1_71_2","unstructured":"Kai Guo Zewen Liu Zhikai Chen Hongzhi Wen Wei Jin Jiliang Tang and Yi Chang. 2024. Learning on graphs with large language models (LLMs): A deep dive into model robustness. arXiv:2407.12068. Retrieved from https:\/\/arxiv.org\/abs\/2407.12068"},{"key":"e_1_3_1_72_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.acl-long.72"},{"key":"e_1_3_1_73_2","unstructured":"Zirui Guo Lianghao Xia Yanhua Yu Yuling Wang Zixuan Yang Wei Wei Liang Pang Tat-Seng Chua and Chao Huang.2024. GraphEdit: Large language models for graph structure learning. arXiv:2402.15183. Retrieved from https:\/\/arxiv.org\/abs\/2402.15183"},{"key":"e_1_3_1_74_2","first-page":"1025","article-title":"Inductive representation learning on large graphs","volume":"30","author":"Hamilton Will","year":"2017","unstructured":"Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. Advances in Neural Information Processing Systems 30 (2017), 1025\u20131035.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_75_2","unstructured":"Haoyu Han Yu Wang Harry Shomer Kai Guo Jiayuan Ding Yongjia Lei Mahantesh Halappanavar Ryan A. Rossi Subhabrata Mukherjee Xianfeng Tang et al. 2024. Retrieval-augmented generation with graphs (graphrag). arXiv:2501.00309. Retrieved from https:\/\/arxiv.org\/abs\/2501.00309"},{"key":"e_1_3_1_76_2","unstructured":"Chen Hao Xie Runfeng Cui Xiangyang Yan Zhou Wang Xin Xuan Zhanwei and Zhang Kai. 2023. LKPNR: LLM and KG for personalized news recommendation framework. arXiv:2308.12028. Retrieved from https:\/\/arxiv.org\/abs\/2308.12028"},{"key":"e_1_3_1_77_2","unstructured":"Mohammad Hashemi Shengbo Gong Juntong Ni Wenqi Fan B. Aditya Prakash and Wei Jin. 2024. A comprehensive survey on graph reduction: Sparsification coarsening and condensation. arXiv:2402.03358. Retrieved from https:\/\/arxiv.org\/abs\/2402.03358"},{"key":"e_1_3_1_78_2","first-page":"4116","volume-title":"International Conference on Machine Learning","author":"Hassani Kaveh","year":"2020","unstructured":"Kaveh Hassani and Amir Hosein Khasahmadi. 2020. Contrastive multi-view representation learning on graphs. In International Conference on Machine Learning. PMLR, 4116\u20134126."},{"key":"e_1_3_1_79_2","unstructured":"Jacqueline He Mengzhou Xia Christiane Fellbaum and Danqi Chen. 2022. Mabel: Attenuating gender bias using textual entailment data. arXiv:2210.14975. Retrieved from https:\/\/arxiv.org\/abs\/2210.14975"},{"key":"e_1_3_1_80_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.findings-emnlp.384"},{"key":"e_1_3_1_81_2","unstructured":"Pengcheng He Xiaodong Liu Jianfeng Gao and Weizhu Chen. 2020. Deberta: Decoding-enhanced bert with disentangled attention. arXiv:2006.03654. Retrieved from https:\/\/arxiv.org\/abs\/2006.03654"},{"key":"e_1_3_1_82_2","unstructured":"Xiaoxin He Xavier Bresson Thomas Laurent Adam Perold Yann LeCun and Bryan Hooi. 2023. Harnessing explanations: LLM-to-LM interpreter for enhanced text-attributed graph representation learning. arXiv:2305.19523. Retrieved from https:\/\/arxiv.org\/abs\/2305.19523"},{"key":"e_1_3_1_83_2","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. arXiv:2402.07630. Retrieved from https:\/\/arxiv.org\/abs\/2402.07630"},{"key":"e_1_3_1_84_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.findings-emnlp.415"},{"key":"e_1_3_1_85_2","volume-title":"GML: Graph Modelling Language","author":"Himsolt Michael","year":"1997","unstructured":"Michael Himsolt. 1997. GML: Graph Modelling Language. University of Passau."},{"key":"e_1_3_1_86_2","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539321"},{"key":"e_1_3_1_87_2","unstructured":"Edward J. Hu Yelong Shen Phillip Wallis Zeyuan Allen-Zhu Yuanzhi Li Shean Wang Lu Wang and Weizhu Chen. 2021. Lora: Low-rank adaptation of large language models. arXiv:2106.09685. Retrieved from https:\/\/arxiv.org\/abs\/2106.09685"},{"key":"e_1_3_1_88_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2023.3310002"},{"key":"e_1_3_1_89_2","unstructured":"Yuntong Hu Zheng Zhang and Liang Zhao. 2023. Beyond text: A deep dive into large language models\u2019 ability on understanding graph data. arXiv:2310.04944. Retrieved from https:\/\/arxiv.org\/abs\/2310.04944"},{"key":"e_1_3_1_90_2","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380027"},{"key":"e_1_3_1_91_2","unstructured":"Ziniu Hu Changjun Fan Ting Chen Kai-Wei Chang and Yizhou Sun. 2019. Pre-training graph neural networks for generic structural feature extraction. arXiv:1905.13728. Retrieved from https:\/\/arxiv.org\/abs\/1905.13728"},{"key":"e_1_3_1_92_2","unstructured":"Wenyue Hua Yingqiang Ge Shuyuan Xu Jianchao Ji and Yongfeng Zhang. 2023. UP5: Unbiased foundation model for fairness-aware recommendation. arXiv:2305.12090. Retrieved from https:\/\/arxiv.org\/abs\/2305.12090"},{"key":"e_1_3_1_93_2","unstructured":"Chengkai Huang Tong Yu Kaige Xie Shuai Zhang Lina Yao and Julian McAuley. 2024. Foundation models for recommender systems: A survey and new perspectives. arXiv:2402.11143. Retrieved from https:\/\/arxiv.org\/abs\/2402.11143"},{"key":"e_1_3_1_94_2","doi-asserted-by":"crossref","unstructured":"Jie Huang Hanyin Shao and Kevin Chen-Chuan Chang. 2022. Are large pre-trained language models leaking your personal information?. arXiv:2205.12628. Retrieved from https:\/\/arxiv.org\/abs\/2205.12628","DOI":"10.18653\/v1\/2022.findings-emnlp.148"},{"key":"e_1_3_1_95_2","unstructured":"Jin Huang Xingjian Zhang Qiaozhu Mei and Jiaqi Ma.2023. Can LLMs effectively leverage graph structural information: When and why. arXiv:2309.16595. Retrieved from https:\/\/arxiv.org\/abs\/2309.16595"},{"key":"e_1_3_1_96_2","unstructured":"Qian Huang Hongyu Ren Peng Chen Gregor Kr\u017emanc Daniel Zeng Percy Liang and Jure Leskovec.2023. PRODIGY: Enabling in-context learning over graphs. arXiv:2305.12600. Retrieved from https:\/\/arxiv.org\/abs\/2305.12600"},{"key":"e_1_3_1_97_2","unstructured":"Neel Jain Avi Schwarzschild Yuxin Wen Gowthami Somepalli John Kirchenbauer Ping-Yeh Chiang Micah Goldblum Aniruddha Saha Jonas Geiping and Tom Goldstein. 2023. Baseline defenses for adversarial attacks against aligned language models. arXiv:2309.00614. Retrieved from https:\/\/arxiv.org\/abs\/2309.00614"},{"key":"e_1_3_1_98_2","doi-asserted-by":"crossref","unstructured":"Jinhao Jiang Kun Zhou Zican Dong Keming Ye Wayne Xin Zhao and Ji-Rong Wen. 2023. Structgpt: A general framework for large language model to reason over structured data. arXiv:2305.09645. Retrieved from https:\/\/arxiv.org\/abs\/2305.09645","DOI":"10.18653\/v1\/2023.emnlp-main.574"},{"key":"e_1_3_1_99_2","unstructured":"Jinhao Jiang Kun Zhou Wayne Xin Zhao Yang Song Chen Zhu Hengshu Zhu and Ji-Rong Wen. 2024. Kg-agent: An efficient autonomous agent framework for complex reasoning over knowledge graph. arXiv:2402.11163. Retrieved from https:\/\/arxiv.org\/abs\/2402.11163"},{"key":"e_1_3_1_100_2","doi-asserted-by":"crossref","unstructured":"Peiling Jiang Jude Rayan Steven P. Dow and Haijun Xia. 2023. Graphologue: Exploring large language model responses with interactive diagrams. arXiv:2305.11473. Retrieved from https:\/\/arxiv.org\/abs\/2305.11473","DOI":"10.1145\/3586183.3606737"},{"key":"e_1_3_1_101_2","doi-asserted-by":"crossref","unstructured":"Zhouyu Jiang Ling Zhong Mengshu Sun Jun Xu Rui Sun Hui Cai Shuhan Luo and Zhiqiang Zhang. 2024. Efficient knowledge infusion via KG-LLM alignment. arXiv:2406.03746. Retrieved from https:\/\/arxiv.org\/abs\/2406.03746","DOI":"10.18653\/v1\/2024.findings-acl.176"},{"key":"e_1_3_1_102_2","unstructured":"Bowen Jin Gang Liu Chi Han Meng Jiang Heng Ji and Jiawei Han. 2023. Large language models on graphs: A comprehensive survey. arXiv:2312.02783. Retrieved from https:\/\/arxiv.org\/abs\/2312.02783"},{"key":"e_1_3_1_103_2","unstructured":"Bowen Jin Wentao Zhang Yu Zhang Yu Meng Xinyang Zhang Qi Zhu and Jiawei Han. 2023. Patton: Language model pretraining on text-rich networks. arXiv:2305.12268. Retrieved from https:\/\/arxiv.org\/abs\/2305.12268"},{"key":"e_1_3_1_104_2","unstructured":"Bowen Jin Wentao Zhang Yu Zhang Yu Meng Han Zhao and Jiawei Han. 2023. Learning multiplex embeddings on text-rich networks with one text encoder. arXiv:2310.06684. Retrieved from https:\/\/arxiv.org\/abs\/2310.06684"},{"key":"e_1_3_1_105_2","unstructured":"Bowen Jin Yu Zhang Yu Meng and Jiawei Han. 2023. Edgeformers: Graph-empowered transformers for representation learning on textual-edge networks. arXiv:2302.11050. Retrieved from https:\/\/arxiv.org\/abs\/2302.11050"},{"key":"e_1_3_1_106_2","first-page":"1020","volume-title":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","author":"Bowen Jin","year":"2023","unstructured":"Bowen Jin, Yu Zhang, Qi Zhu, and Jiawei Han. 2023. Heterformer: Transformer-based deep node representation learning on heterogeneous text-rich networks. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 1020\u20131031."},{"key":"e_1_3_1_107_2","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00300"},{"key":"e_1_3_1_108_2","doi-asserted-by":"crossref","unstructured":"Minki Kang Jinheon Baek and Sung Ju Hwang. 2022. KALA: Knowledge-augmented language model adaptation. arXiv:2204.10555. Retrieved from https:\/\/arxiv.org\/abs\/2204.10555","DOI":"10.18653\/v1\/2022.naacl-main.379"},{"key":"e_1_3_1_109_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.lindif.2023.102274"},{"key":"e_1_3_1_110_2","doi-asserted-by":"crossref","unstructured":"Jiho Kim Yeonsu Kwon Yohan Jo and Edward Choi. 2023. KG-GPT: A general framework for reasoning on knowledge graphs using large language models. arXiv:2310.11220. Retrieved from https:\/\/arxiv.org\/abs\/2310.11220","DOI":"10.18653\/v1\/2023.findings-emnlp.631"},{"key":"e_1_3_1_111_2","unstructured":"Thomas N. Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv:1609.02907. Retrieved from https:\/\/arxiv.org\/abs\/1609.02907"},{"key":"e_1_3_1_112_2","first-page":"21618","article-title":"Rethinking graph transformers with spectral attention","volume":"34","author":"Kreuzer Devin","year":"2021","unstructured":"Devin Kreuzer, Dominique Beaini, Will Hamilton, Vincent L\u00e9tourneau, and Prudencio Tossou. 2021. Rethinking graph transformers with spectral attention. Advances in Neural Information Processing Systems 34 (2021), 21618\u201321629.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_113_2","unstructured":"Junnan Li Dongxu Li Silvio Savarese and Steven Hoi. 2023. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. arXiv:2301.12597. Retrieved from https:\/\/arxiv.org\/abs\/2301.12597"},{"key":"e_1_3_1_114_2","unstructured":"Jiatong Li Yunqing Liu Wenqi Fan Xiao-Yong Wei Hui Liu Jiliang Tang and Qing Li. 2023. Empowering molecule discovery for molecule-caption translation with large language models: A ChatGPT perspective. arXiv:2306.06615. Retrieved from https:\/\/arxiv.org\/abs\/2306.06615"},{"key":"e_1_3_1_115_2","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbab109"},{"key":"e_1_3_1_116_2","first-page":"8880","article-title":"OERL: Enhanced representation learning via open knowledge graphs","author":"Li Qian","year":"2022","unstructured":"Qian Li, Daling Wang, Shi Feng Kaisong Song, Yifei Zhang, and Ge Yu. 2022. OERL: Enhanced representation learning via open knowledge graphs. IEEE Transactions on Knowledge and Data Engineering 35, 9 (2022), 8880\u20138892.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_1_117_2","unstructured":"Shengrui Li Xueting Han and Jing Bai. 2023. AdapterGNN: Efficient delta tuning improves generalization ability in graph neural networks. arXiv:2304.09595. Retrieved from https:\/\/arxiv.org\/abs\/2304.09595"},{"key":"e_1_3_1_118_2","unstructured":"Xinze Li Yixin Cao Liangming Pan Yubo Ma and Aixin Sun. 2023. Towards verifiable generation: A benchmark for knowledge-aware language model attribution. arXiv:2310.05634. Retrieved from https:\/\/arxiv.org\/abs\/2310.05634"},{"key":"e_1_3_1_119_2","unstructured":"Yuhan Li Zhixun Li Peisong Wang Jia Li Xiangguo Sun Hong Cheng and Jeffrey Xu Yu. 2023. A survey of graph meets large language model: Progress and future directions. arXiv:2311.12399. Retrieved from https:\/\/arxiv.org\/abs\/2311.12399"},{"key":"e_1_3_1_120_2","unstructured":"Yuan Li Xiaodan Liang Zhiting Hu Yinbo Chen and Eric P. Xing. 2018. Graph transformer. Retrieved from https:\/\/openreview.net\/forum?id=HJei-2RcK7"},{"key":"e_1_3_1_121_2","unstructured":"Yansong Li Zhixing Tan and Yang Liu. 2023. Privacy-preserving prompt tuning for large language model services. arXiv:2305.06212. Retrieved from https:\/\/arxiv.org\/abs\/2305.06212."},{"key":"e_1_3_1_122_2","unstructured":"Yuhan Li Peisong Wang Xiao Zhu Aochuan Chen Haiyun Jiang Deng Cai Victor Wai Kin Chan and Jia Li. 2024. Glbench: A comprehensive benchmark for graph with large language models. arXiv:2407.07457. Retrieved from https:\/\/arxiv.org\/abs\/2407.07457"},{"key":"e_1_3_1_123_2","doi-asserted-by":"crossref","unstructured":"Youwei Liang Ruiyi Zhang Li Zhang and Pengtao Xie. 2023. DrugChat: Towards enabling ChatGPT-like capabilities on drug molecule graphs. arXiv:2309.03907. Retrieved from https:\/\/arxiv.org\/abs\/2309.03907","DOI":"10.36227\/techrxiv.22945922"},{"key":"e_1_3_1_124_2","unstructured":"Chang Liu and Bo Wu. 2023. Evaluating large language models on graphs: Performance insights and comparative analysis. arXiv:2308.11224. Retrieved from https:\/\/arxiv.org\/abs\/2308.11224"},{"key":"e_1_3_1_125_2","unstructured":"Chengyi Liu Jiahao Zhang Shijie Wang Wenqi Fan and Qing Li. 2024. Score-based generative diffusion models for social recommendations. arXiv:2412.15579. Retrieved from https:\/\/arxiv.org\/abs\/2412.15579"},{"key":"e_1_3_1_126_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.coling-main.390"},{"key":"e_1_3_1_127_2","unstructured":"Hao Liu Jiarui Feng Lecheng Kong Ningyue Liang Dacheng Tao Yixin Chen and Muhan Zhang. 2023. One for all: Towards training one graph model for all classification tasks. arXiv:2310.00149. Retrieved from https:\/\/arxiv.org\/abs\/2310.00149"},{"key":"e_1_3_1_128_2","unstructured":"Haochen Liu Yiqi Wang Wenqi Fan Xiaorui Liu Yaxin Li Shaili Jain Yunhao Liu Anil K. Jain and Jiliang Tang. 2021. Trustworthy AI: A computational perspective. arXiv:2107.06641. Retrieved from https:\/\/arxiv.org\/abs\/2107.06641"},{"key":"e_1_3_1_129_2","unstructured":"Junling Liu Chao Liu Renjie Lv Kang Zhou and Yan Zhang. 2023. Is ChatGPT a good recommender? A preliminary study. arXiv:2304.10149. Retrieved from https:\/\/arxiv.org\/abs\/2304.10149"},{"key":"e_1_3_1_130_2","unstructured":"Jiawei Liu Cheng Yang Zhiyuan Lu Junze Chen Yibo Li Mengmei Zhang Ting Bai Yuan Fang Lichao Sun Philip S. Yu et al. 2023. Towards graph foundation models: A survey and beyond. arXiv:2310.11829. Retrieved from https:\/\/arxiv.org\/abs\/2310.11829"},{"key":"e_1_3_1_131_2","unstructured":"Nelson Liu Kevin Lin John Hewitt Ashwin Paranjape Michele Bevilacqua Fabio Petroni and Percy Liang. 2023. Lost in the middle: How language models use long contexts. arXiv:2307.03172. Retrieved from https:\/\/arxiv.org\/abs\/2307.03172"},{"key":"e_1_3_1_132_2","unstructured":"Pengfei Liu Yiming Ren and Zhixiang Ren. 2023. GIT-Mol: A multi-modal large language model for molecular science with graph image and text. arXiv:2308.06911. Retrieved from https:\/\/arxiv.org\/abs\/2308.06911"},{"key":"e_1_3_1_133_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i03.5681"},{"key":"e_1_3_1_134_2","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583386"},{"key":"e_1_3_1_135_2","doi-asserted-by":"crossref","unstructured":"Robert L. Logan Iv Nelson F. Liu Matthew E. Peters Matt Gardner and Sameer Singh. 2019. Barack\u2019s wife hillary: Using knowledge-graphs for fact-aware language modeling. arXiv:1906.07241. Retrieved from https:\/\/arxiv.org\/abs\/1906.07241","DOI":"10.18653\/v1\/P19-1598"},{"key":"e_1_3_1_136_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.findings-emnlp.325"},{"key":"e_1_3_1_137_2","first-page":"28748","article-title":"When do graph neural networks help with node classification? investigating the homophily principle on node distinguishability","volume":"36","author":"Luan Sitao","year":"2024","unstructured":"Sitao Luan, Chenqing Hua, Minkai Xu, Qincheng Lu, Jiaqi Zhu, Xiao-Wen Chang, Jie Fu, Jure Leskovec, and Doina Precup. 2024. When do graph neural networks help with node classification? investigating the homophily principle on node distinguishability. Advances in Neural Information Processing Systems 36 (2024), 28748\u201328760.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_138_2","first-page":"19620","article-title":"Parameterized explainer for graph neural network","volume":"33","author":"Luo Dongsheng","year":"2020","unstructured":"Dongsheng Luo, Wei Cheng, Dongkuan Xu, Wenchao Yu, Bo Zong, Haifeng Chen, and Xiang Zhang. 2020. Parameterized explainer for graph neural network. Advances in Neural Information Processing Systems 33 (2020), 19620\u201319631.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_139_2","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441734"},{"key":"e_1_3_1_140_2","unstructured":"Haoran Luo Haihong E Zichen Tang Shiyao Peng Yikai Guo Wentai Zhang Chenghao Ma Guanting Dong Meina Song and Wei Lin. 2023. ChatKBQA: A generate-then-retrieve framework for knowledge base question answering with fine-tuned large language models. arXiv:2310.08975. Retrieved from https:\/\/arxiv.org\/abs\/2310.08975"},{"key":"e_1_3_1_141_2","unstructured":"Linhao Luo Jiaxin Ju Bo Xiong Yuan-Fang Li Gholamreza Haffari and Shirui Pan. 2023. ChatRule: Mining logical rules with large language models for knowledge graph reasoning. arXiv:2309.01538. Retrieved from https:\/\/arxiv.org\/abs\/2309.01538"},{"key":"e_1_3_1_142_2","unstructured":"Linhao Luo Yuan-Fang Li Gholamreza Haffari and Shirui Pan. 2023. Reasoning on graphs: Faithful and interpretable large language model reasoning. arXiv:2310.01061. Retrieved from https:\/\/arxiv.org\/abs\/2310.01061"},{"key":"e_1_3_1_143_2","unstructured":"Yizhen Luo Jiahuan Zhang Siqi Fan Kai Yang Yushuai Wu Mu Qiao and Zaiqing Nie. 2023. Biomedgpt: Open multimodal generative pre-trained transformer for biomedicine. arXiv:2308.09442. Retrieved from https:\/\/arxiv.org\/abs\/2308.09442"},{"key":"e_1_3_1_144_2","unstructured":"Hanjia Lyu Song Jiang Hanqing Zeng Yinglong Xia and Jiebo Luo. 2023. LLM-Rec: Personalized recommendation via prompting large language models. arXiv:2307.15780. Retrieved from https:\/\/arxiv.org\/abs\/2307.15780"},{"key":"e_1_3_1_145_2","unstructured":"Xinyin Ma Gongfan Fang and Xinchao Wang. 2023. LLM-Pruner: On the structural pruning of large language models. arXiv:2305.11627. Retrieved from https:\/\/arxiv.org\/abs\/2305.11627"},{"key":"e_1_3_1_146_2","doi-asserted-by":"publisher","DOI":"10.1017\/9781108924184"},{"key":"e_1_3_1_147_2","unstructured":"Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras and Adrian Vladu. 2017. Towards deep learning models resistant to adversarial attacks. arXiv:1706.06083. Retrieved from https:\/\/arxiv.org\/abs\/1706.06083"},{"key":"e_1_3_1_148_2","unstructured":"Haitao Mao Zhikai Chen Wenzhuo Tang Jianan Zhao Yao Ma Tong Zhao Neil Shah Michael Galkin and Jiliang Tang. 2024. Graph foundation models. arXiv:2402.02216. Retrieved from https:\/\/arxiv.org\/abs\/2402.02216"},{"key":"e_1_3_1_149_2","unstructured":"Haitao Mao Juanhui Li Harry Shomer Bingheng Li Wenqi Fan Yao Ma Tong Zhao Neil Shah and Jiliang Tang. 2023. Revisiting link prediction: A data perspective. arXiv:2310.00793. Retrieved from https:\/\/arxiv.org\/abs\/2310.00793"},{"key":"e_1_3_1_150_2","first-page":"2156","article-title":"Provably powerful graph networks","volume":"32","author":"Maron Haggai","year":"2019","unstructured":"Haggai Maron, Heli Ben-Hamu, Hadar Serviansky, and Yaron Lipman. 2019. Provably powerful graph networks. Advances in Neural Information Processing Systems 32 (2019), 2156\u20132167.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_151_2","doi-asserted-by":"crossref","unstructured":"Costas Mavromatis Vassilis N. Ioannidis Shen Wang Da Zheng Soji Adeshina Jun Ma Han Zhao Christos Faloutsos and George Karypis. 2023. Train your own GNN teacher: Graph-aware distillation on textual graphs. arXiv:2304.10668. Retrieved from https:\/\/arxiv.org\/abs\/2304.10668","DOI":"10.1007\/978-3-031-43418-1_10"},{"key":"e_1_3_1_152_2","unstructured":"Costas Mavromatis and George Karypis. 2024. Gnn-rag: Graph neural retrieval for large language model reasoning. arXiv:2405.20139. Retrieved from https:\/\/arxiv.org\/abs\/2405.20139"},{"key":"e_1_3_1_153_2","unstructured":"Lars-Peter Meyer Johannes Frey Kurt Junghanns Felix Brei Kirill Bulert Sabine Gr\u00fcnder-Fahrer and Michael Martin. 2023. Developing a scalable benchmark for assessing large language models in knowledge graph engineering. arXiv:2308.16622. Retrieved from https:\/\/arxiv.org\/abs\/2308.16622"},{"key":"e_1_3_1_154_2","unstructured":"Sai Mitheran Abhinav Java Surya Kant Sahu and Arshad Shaikh. 2021. Introducing self-attention to target attentive graph neural networks. arXiv:2107.01516. Retrieved from https:\/\/arxiv.org\/abs\/2107.01516"},{"key":"e_1_3_1_155_2","doi-asserted-by":"crossref","unstructured":"Fedor Moiseev Zhe Dong Enrique Alfonseca and Martin Jaggi. 2022. SKILL: Structured knowledge infusion for large language models. arXiv:2205.08184. Retrieved from https:\/\/arxiv.org\/abs\/2205.08184","DOI":"10.18653\/v1\/2022.naacl-main.113"},{"key":"e_1_3_1_156_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33014602"},{"key":"e_1_3_1_157_2","unstructured":"Zhe Ni Xiao-Xin Deng Cong Tai Xin-Yue Zhu Xiang Wu Yong-Jin Liu and Long Zeng. 2023. GRID: Scene-graph-based instruction-driven robotic task planning. arXiv:2309.07726. Retrieved from https:\/\/arxiv.org\/abs\/2309.07726"},{"key":"e_1_3_1_158_2","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671837"},{"key":"e_1_3_1_159_2","unstructured":"Shirui Pan Linhao Luo Yufei Wang Chen Chen Jiapu Wang and Xindong Wu. 2023. Unifying large language models and knowledge graphs: A roadmap. arXiv:2306.08302. Retrieved from https:\/\/arxiv.org\/abs\/2306.08302"},{"key":"e_1_3_1_160_2","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623732"},{"key":"e_1_3_1_161_2","unstructured":"Bryan Perozzi Bahare Fatemi Dustin Zelle Anton Tsitsulin Mehran Kazemi Rami Al-Rfou and Jonathan Halcrow. 2024. Let your graph do the talking: Encoding structured data for LLMs. arXiv:2402.05862. Retrieved from https:\/\/arxiv.org\/abs\/2402.05862"},{"key":"e_1_3_1_162_2","doi-asserted-by":"crossref","unstructured":"Nina Poerner Ulli Waltinger and Hinrich Sch\u00fctze. 2019. E-BERT: Efficient-yet-effective entity embeddings for BERT. arXiv:1911.03681. Retrieved from https:\/\/arxiv.org\/abs\/1911.03681","DOI":"10.18653\/v1\/2020.findings-emnlp.71"},{"key":"e_1_3_1_163_2","unstructured":"Chen Qian Huayi Tang Zhirui Yang Hong Liang and Yong Liu. 2023. Can large language models empower molecular property prediction? arXiv:2307.07443. Retrieved from https:\/\/arxiv.org\/abs\/2307.07443"},{"key":"e_1_3_1_164_2","unstructured":"Yujia Qin Yankai Lin Ryuichi Takanobu Zhiyuan Liu Peng Li Heng Ji Minlie Huang Maosong Sun and Jie Zhou. 2020. ERICA: Improving entity and relation understanding for pre-trained language models via contrastive learning. arXiv:2012.15022. Retrieved from https:\/\/arxiv.org\/abs\/2012.15022"},{"key":"e_1_3_1_165_2","unstructured":"Yijian Qin Xin Wang Ziwei Zhang and Wenwu Zhu. 2023. Disentangled representation learning with large language models for text-attributed graphs. arXiv:2310.18152. Retrieved from https:\/\/arxiv.org\/abs\/2310.18152"},{"key":"e_1_3_1_166_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403168"},{"key":"e_1_3_1_167_2","unstructured":"Haohao Qu Wenqi Fan Zihuai Zhao and Qing Li. 2024. Tokenrec: Learning to tokenize id for llm-based generative recommendation. arXiv:2406.10450. Retrieved from https:\/\/arxiv.org\/abs\/2406.10450"},{"key":"e_1_3_1_168_2","unstructured":"Haohao Qu Liangbo Ning Rui An Wenqi Fan Tyler Derr Hui Liu Xin Xu and Qing Li. 2024. 2024. A survey of mamba. arXiv:2408.01129. Retrieved from https:\/\/arxiv.org\/abs\/2408.01129"},{"key":"e_1_3_1_169_2","unstructured":"Haohao Qu Yifeng Zhang Liangbo Ning Wenqi Fan and Qing Li. 2024. Ssd4rec: A structured state space duality model for efficient sequential recommendation. arXiv:2409.01192. Retrieved from https:\/\/arxiv.org\/abs\/2409.01192"},{"key":"e_1_3_1_170_2","unstructured":"Alec Radford Karthik Narasimhan Tim Salimans and Ilya Sutskever. 2018. Improving language understanding by generative pre-training. Retrieved from https:\/\/paperswithcode.com\/paper\/improving-language-understanding-by"},{"key":"e_1_3_1_171_2","doi-asserted-by":"publisher","DOI":"10.5555\/3455716.3455856"},{"key":"e_1_3_1_172_2","unstructured":"Krishan Rana Jesse Haviland Sourav Garg Jad Abou-Chakra Ian Reid and Niko Suenderhauf. 2023. SayPlan: Grounding large language models using 3D scene graphs for scalable robot task planning. arXiv:2307.06135. Retrieved from https:\/\/arxiv.org\/abs\/2307.06135"},{"key":"e_1_3_1_173_2","unstructured":"Xubin Ren Wei Wei Lianghao Xia Lixin Su Suqi Cheng Junfeng Wang Dawei Yin and Chao Huang. 2023. Representation learning with large language models for recommendation. arXiv:2310.15950. Retrieved from https:\/\/arxiv.org\/abs\/2310.15950"},{"key":"e_1_3_1_174_2","first-page":"12559","article-title":"Self-supervised graph transformer on large-scale molecular data","volume":"33","author":"Rong Yu","year":"2020","unstructured":"Yu Rong, Yatao Bian, Tingyang Xu, Weiyang Xie, Ying Wei, Wenbing Huang, and Junzhou Huang. 2020. Self-supervised graph transformer on large-scale molecular data. Advances in Neural Information Processing Systems 33 (2020), 12559\u201312571.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_175_2","unstructured":"Corby Rosset Chenyan Xiong Minh Phan Xia Song Paul Bennett and Saurabh Tiwary. 2020. Knowledge-aware language model pretraining. arXiv:2007.00655. Retrieved from https:\/\/arxiv.org\/abs\/2007.00655"},{"key":"e_1_3_1_176_2","unstructured":"Andre Niyongabo Rubungo Craig Arnold Barry P. Rand and Adji Bousso Dieng. 2023. LLM-Prop: Predicting physical and electronic properties of crystalline solids from their text descriptions. arXiv:2310.14029. Retrieved from https:\/\/arxiv.org\/abs\/2310.14029"},{"key":"e_1_3_1_177_2","doi-asserted-by":"crossref","unstructured":"Junyuan Shang Tengfei Ma Cao Xiao and Jimeng Sun. 2019. Pre-training of graph augmented transformers for medication recommendation. arXiv:1906.00346. Retrieved from https:\/\/arxiv.org\/abs\/1906.00346","DOI":"10.24963\/ijcai.2019\/825"},{"key":"e_1_3_1_178_2","doi-asserted-by":"crossref","unstructured":"Tao Shen Yi Mao Pengcheng He Guodong Long Adam Trischler and Weizhu Chen. 2020. Exploiting structured knowledge in text via graph-guided representation learning. arXiv:2004.14224. Retrieved from https:\/\/arxiv.org\/abs\/2004.14224","DOI":"10.18653\/v1\/2020.emnlp-main.722"},{"key":"e_1_3_1_179_2","unstructured":"Yucheng Shi Hehuan Ma Wenliang Zhong Gengchen Mai Xiang Li Tianming Liu and Junzhou Huang. 2023. Chatgraph: Interpretable text classification by converting chatgpt knowledge to graphs. arXiv:2305.03513. Retrieved from https:\/\/arxiv.org\/abs\/2305.03513"},{"key":"e_1_3_1_180_2","unstructured":"Yaorui Shi An Zhang Enzhi Zhang Zhiyuan Liu and Xiang Wang. 2023. ReLM: Leveraging language models for enhanced chemical reaction prediction. arXiv:2310.13590. Retrieved from https:\/\/arxiv.org\/abs\/2310.13590"},{"key":"e_1_3_1_181_2","first-page":"8634","volume-title":"37th Conference on Neural Information Processing Systems","author":"Shinn Noah","year":"2023","unstructured":"Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik R. Narasimhan, and Shunyu Yao. 2023. Reflexion: Language agents with verbal reinforcement learning. In 37th Conference on Neural Information Processing Systems, 8634\u20138652."},{"key":"e_1_3_1_182_2","unstructured":"Reza Shirkavand and Heng Huang. 2023. Deep prompt tuning for graph transformers. arXiv:2309.10131. Retrieved from https:\/\/arxiv.org\/abs\/2309.10131"},{"key":"e_1_3_1_183_2","unstructured":"Yunchong Song Chenghu Zhou Xinbing Wang and Zhouhan Lin. 2023. Ordered gnn: Ordering message passing to deal with heterophily and over-smoothing. arXiv:2302.01524. Retrieved from https:\/\/arxiv.org\/abs\/2302.01524"},{"key":"e_1_3_1_184_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_1_185_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.aiopen.2021.06.004"},{"key":"e_1_3_1_186_2","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539249"},{"key":"e_1_3_1_187_2","unstructured":"Shengyin Sun Yuxiang Ren Chen Ma and Xuecang Zhang. 2023. Large language models as topological structure enhancers for text-attributed graphs. arXiv:2311.14324. Retrieved from https:\/\/arxiv.org\/abs\/2311.14324"},{"key":"e_1_3_1_188_2","doi-asserted-by":"crossref","unstructured":"Xiangguo Sun Hong Cheng Jia Li Bo Liu and Jihong Guan. 2023. All in One: Multi-Task Prompting for Graph Neural Networks. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining 2120\u20132131.","DOI":"10.1145\/3580305.3599256"},{"key":"e_1_3_1_189_2","unstructured":"Yu Sun Shuohuan Wang Shikun Feng Siyu Ding Chao Pang Junyuan Shang Jiaxiang Liu Xuyi Chen Yanbin Zhao Yuxiang Lu et al. 2021. ERNIE 3.0: Large-scale knowledge enhanced pre-training for language understanding and generation. arXiv:2107.02137. Retrieved from https:\/\/arxiv.org\/abs\/2107.02137"},{"key":"e_1_3_1_190_2","doi-asserted-by":"publisher","DOI":"10.1145\/2736277.2741093"},{"key":"e_1_3_1_191_2","unstructured":"Jiabin Tang Yuhao Yang Wei Wei Lei Shi Lixin Su Suqi Cheng Dawei Yin and Chao Huang. 2023. GraphGPT: Graph instruction tuning for large language models. arXiv:2310.13023. Retrieved from https:\/\/arxiv.org\/abs\/2310.13023"},{"key":"e_1_3_1_192_2","first-page":"2023","article-title":"A general single-cell analysis framework via conditional diffusion generative models","author":"Tang Wenzhuo","year":"2023","unstructured":"Wenzhuo Tang, Renming Liu, Hongzhi Wen, Xinnan Dai, Jiayuan Ding, Hang Li, Wenqi Fan, Yuying Xie, and Jiliang Tang. 2023. A general single-cell analysis framework via conditional diffusion generative models. bioRxiv (2023), 2023\u20132010.","journal-title":"bioRxiv"},{"key":"e_1_3_1_193_2","unstructured":"Zuoli Tang Zhaoxin Huan Zihao Li Xiaolu Zhang Jun Hu Chilin Fu Jun Zhou and Chenliang Li. 2023. One model for all: Large language models are domain-agnostic recommendation systems. arXiv:2310.14304. Retrieved from https:\/\/arxiv.org\/abs\/2310.14304"},{"key":"e_1_3_1_194_2","unstructured":"Romal Thoppilan Daniel De Freitas Jamie Hall Noam Shazeer Apoorv Kulshreshtha Heng-Tze Cheng Alicia Jin Taylor Bos Leslie Baker Yu Du et al. 2022. Lamda: Language models for dialog applications. arXiv:2201.08239. Retrieved from https:\/\/arxiv.org\/abs\/2201.08239"},{"key":"e_1_3_1_195_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.findings-acl.229"},{"key":"e_1_3_1_196_2","unstructured":"Yijun Tian Huan Song Zichen Wang Haozhu Wang Ziqing Hu Fang Wang Nitesh V. Chawla and Panpan Xu. 2023. Graph neural prompting with large language models. arXiv:2309.15427. Retrieved from https:\/\/arxiv.org\/abs\/2309.15427"},{"key":"e_1_3_1_197_2","unstructured":"Hugo Touvron Thibaut Lavril Gautier Izacard Xavier Martinet Marie-Anne Lachaux Timoth\u00e9e Lacroix Baptiste Rozi\u00e8re Naman Goyal Eric Hambro Faisal Azhar et al. 2023. Llama: Open and efficient foundation language models. arXiv:2302.13971. Retrieved from https:\/\/arxiv.org\/abs\/2302.13971"},{"key":"e_1_3_1_198_2","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bty535"},{"key":"e_1_3_1_199_2","first-page":"6000","article-title":"Attention is all you need","volume":"30","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in Neural Information Processing Systems 30 (2017), 6000\u20136010.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_200_2","unstructured":"Petar Veli\u010dkovi\u0107 Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Lio and Yoshua Bengio. 2017. Graph attention networks. arXiv:1710.10903. Retrieved from https:\/\/arxiv.org\/abs\/1710.10903"},{"key":"e_1_3_1_201_2","unstructured":"Petar Veli\u010dkovi\u0107 William Fedus William L. Hamilton Pietro Li\u00f2 Yoshua Bengio and R. Devon Hjelm. 2018. Deep graph infomax. arXiv:1809.10341. Retrieved from https:\/\/arxiv.org\/abs\/1809.10341"},{"key":"e_1_3_1_202_2","doi-asserted-by":"crossref","unstructured":"Boxin Wang Chejian Xu Xiangyu Liu Yu Cheng and Bo Li. 2022. SemAttack: Natural textual attacks via different semantic spaces. arXiv:2205.01287. Retrieved from https:\/\/arxiv.org\/abs\/2205.01287","DOI":"10.18653\/v1\/2022.findings-naacl.14"},{"key":"e_1_3_1_203_2","unstructured":"Chaojie Wang Yishi Xu Zhong Peng Chenxi Zhang Bo Chen Xinrun Wang Lei Feng and Bo An. 2023. keqing: Knowledge-based question answering is a nature chain-of-thought mentor of LLM. arXiv:2401.00426. Retrieved from https:\/\/arxiv.org\/abs\/2401.00426"},{"key":"e_1_3_1_204_2","doi-asserted-by":"crossref","unstructured":"Fali Wang Runxue Bao Suhang Wang Wenchao Yu Yanchi Liu Wei Cheng and Haifeng Chen. 2024. Infuserki: Enhancing large language models with knowledge graphs via infuser-guided knowledge integration. arXiv:2402.11441. Retrieved from https:\/\/arxiv.org\/abs\/2402.11441","DOI":"10.18653\/v1\/2024.findings-emnlp.209"},{"key":"e_1_3_1_205_2","unstructured":"Heng Wang Shangbin Feng Tianxing He Zhaoxuan Tan Xiaochuang Han and Yulia Tsvetkov. 2023. Can language models solve graph problems in natural language? arXiv:2305.10037. Retrieved from https:\/\/arxiv.org\/abs\/2305.10037"},{"key":"e_1_3_1_206_2","unstructured":"Hanbing Wang Xiaorui Liu Wenqi Fan Xiangyu Zhao Venkataramana Kini Devendra Yadav Fei Wang Zhen Wen Jiliang Tang and Hui Liu. 2024. Rethinking large language model architectures for sequential recommendations. arXiv:2402.09543. Retrieved from https:\/\/arxiv.org\/abs\/2402.09543"},{"key":"e_1_3_1_207_2","unstructured":"Lin Wang Wenqi Fan Jiatong Li Yao Ma and Qing Li. 2023. Fast graph condensation with structure-based neural tangent kernel. arXiv:2310.11046. Retrieved from https:\/\/arxiv.org\/abs\/2310.11046"},{"key":"e_1_3_1_208_2","unstructured":"Qinyong Wang Zhenxiang Gao and Rong Xu. 2023. Graph agent: Explicit reasoning agent for graphs. arXiv:2310.16421. Retrieved from https:\/\/arxiv.org\/abs\/2310.16421"},{"key":"e_1_3_1_209_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.findings-acl.121"},{"key":"e_1_3_1_210_2","unstructured":"Shijie Wang Wenqi Fan Yue Feng Xinyu Ma Shuaiqiang Wang and Dawei Yin. 2025. Knowledge graph retrieval-augmented generation for LLM-based recommendation. arXiv:2501.02226. Retrieved from https:\/\/arxiv.org\/abs\/2501.02226"},{"issue":"1","key":"e_1_3_1_211_2","doi-asserted-by":"crossref","first-page":", 1","DOI":"10.1145\/3696105","article-title":"Multi-agent attacks for black-box social recommendations","volume":"43","author":"Wang Shijie","year":"2024","unstructured":"Shijie Wang, Wenqi Fan, Xiao-Yong Wei, Xiaowei Mei, Shanru Lin, and Qing Li. 2024. Multi-agent attacks for black-box social recommendations. ACM Transactions on Information Systems 43, 1 (2024), 1\u201326.","journal-title":"ACM Transactions on Information Systems"},{"key":"e_1_3_1_212_2","unstructured":"Kellie Webster Xuezhi Wang Ian Tenney Alex Beutel Emily Pitler Ellie Pavlick Jilin Chen Ed Chi and Slav Petrov. 2020. Measuring and reducing gendered correlations in pre-trained models. arXiv:2010.06032. Retrieved from https:\/\/arxiv.org\/abs\/2010.06032"},{"key":"e_1_3_1_213_2","unstructured":"Alexander Wei Nika Haghtalab and Jacob Steinhardt. 2023. Jailbroken: How does llm safety training fail?. arXiv:2307.02483. Retrieved from https:\/\/arxiv.org\/abs\/2307.02483"},{"key":"e_1_3_1_214_2","unstructured":"Shaopeng Wei Yu Zhao Xingyan Chen Qing Li Fuzhen Zhuang Ji Liu Fuji Ren and Gang Kou. 2022. Graph learning and its advancements on large language models: A holistic survey. arXiv:2212.08966. Retrieved from https:\/\/arxiv.org\/abs\/2212.08966"},{"key":"e_1_3_1_215_2","doi-asserted-by":"crossref","unstructured":"Wei Wei Xubin Ren Jiabin Tang Qinyong Wang Lixin Su Suqi Cheng Junfeng Wang Dawei Yin and Chao Huang. 2024. LLMRec: Large language models with graph augmentation for recommendation. In Proceedings of the 17th International Conference on Web Search and Data Mining (WSDM) 806\u2013815.","DOI":"10.1145\/3616855.3635853"},{"key":"e_1_3_1_216_2","unstructured":"Yilin Wen Zifeng Wang and Jimeng Sun. 2023. MindMap: Knowledge graph prompting sparks graph of thoughts in large language models. arXiv:2308.09729. Retrieved from https:\/\/arxiv.org\/abs\/2308.09729"},{"key":"e_1_3_1_217_2","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3542597"},{"key":"e_1_3_1_218_2","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557583"},{"key":"e_1_3_1_219_2","unstructured":"Jiahao Wu Qijiong Liu Hengchang Hu Wenqi Fan Shengcai Liu Qing Li Xiao-Ming Wu and Ke Tang. 2023. Leveraging large language models (LLMs) to empower training-free dataset condensation for content-based recommendation. arXiv:2310.09874. Retrieved from https:\/\/arxiv.org\/abs\/2310.09874"},{"key":"e_1_3_1_220_2","unstructured":"Likang Wu Zhaopeng Qiu Zhi Zheng Hengshu Zhu and Enhong Chen. 2023. Exploring large language model for graph data understanding in online job recommendations. arXiv:2307.05722. Retrieved from https:\/\/arxiv.org\/abs\/2307.05722"},{"key":"e_1_3_1_221_2","unstructured":"Shijie Wu Ozan Irsoy Steven Lu Vadim Dabravolski Mark Dredze Sebastian Gehrmann Prabhanjan Kambadur David Rosenberg and Gideon Mann. 2023. Bloomberggpt: A large language model for finance. arXiv:2303.17564. Retrieved from https:\/\/arxiv.org\/abs\/2303.17564"},{"key":"e_1_3_1_222_2","unstructured":"Yike Wu Nan Hu Sheng Bi Guilin Qi Jie Ren Anhuan Xie and Wei Song. 2023. Retrieve-rewrite-answer: A KG-to-text enhanced LLMs framework for knowledge graph question answering. arXiv:2309.11206. Retrieved from https:\/\/arxiv.org\/abs\/2309.11206"},{"key":"e_1_3_1_223_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"e_1_3_1_224_2","unstructured":"Yunjia Xi Weiwen Liu Jianghao Lin Jieming Zhu Bo Chen Ruiming Tang Weinan Zhang Rui Zhang and Yong Yu. 2023. Towards open-world recommendation with knowledge augmentation from large language models. arXiv:2306.10933. Retrieved from https:\/\/arxiv.org\/abs\/2306.10933"},{"key":"e_1_3_1_225_2","unstructured":"Lianghao Xia Ben Kao and Chao Huang. 2024. OpenGraph: Towards open graph foundation models. arXiv:2403.01121. Retrieved from https:\/\/arxiv.org\/abs\/2403.01121"},{"key":"e_1_3_1_226_2","unstructured":"Mengzhou Xia Tianyu Gao Zhiyuan Zeng and Danqi Chen. 2023. Sheared llama: Accelerating language model pre-training via structured pruning. arXiv:2310.06694. Retrieved from https:\/\/arxiv.org\/abs\/2310.06694"},{"key":"e_1_3_1_227_2","unstructured":"Keyulu Xu Weihua Hu Jure Leskovec and Stefanie Jegelka. 2018. How powerful are graph neural networks? arXiv:1810.00826. Retrieved from https:\/\/arxiv.org\/abs\/1810.00826"},{"issue":"5","key":"e_1_3_1_228_2","first-page":"4539","article-title":"Ccgl: Contrastive Cascade graph learning","volume":"35","author":"Xu Xovee","year":"2022","unstructured":"Xovee Xu, Fan Zhou, Kunpeng Zhang, and Siyuan Liu. 2022. Ccgl: Contrastive Cascade graph learning. IEEE Transactions on Knowledge and Data Engineering 35, 5 (2022), 4539\u20134554.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_1_229_2","doi-asserted-by":"crossref","unstructured":"Yan Xu Mahdi Namazifar Devamanyu Hazarika Aishwarya Padmakumar Yang Liu and Dilek Hakkani-T\u00fcr. 2023. KILM: Knowledge injection into encoder-decoder language models. arXiv:2302.09170. Retrieved from https:\/\/arxiv.org\/abs\/2302.09170","DOI":"10.18653\/v1\/2023.acl-long.275"},{"key":"e_1_3_1_230_2","unstructured":"Ruizhan Xue Huimin Deng Fang He Maojun Wang and Zeyu Zhang. 2025. Trustworthy GNNs with LLMs: A systematic review and taxonomy. arXiv:2502.08353. Retrieved from https:\/\/arxiv.org\/abs\/2502.08353"},{"key":"e_1_3_1_231_2","unstructured":"Rui Xue Xipeng Shen Ruozhou Yu and Xiaorui Liu. 2023. Efficient large language models fine-tuning on graphs. arXiv:2312.04737. Retrieved from https:\/\/arxiv.org\/abs\/2312.04737"},{"key":"e_1_3_1_232_2","doi-asserted-by":"crossref","unstructured":"Ikuya Yamada Akari Asai Hiroyuki Shindo Hideaki Takeda and Yuji Matsumoto. 2020. LUKE: Deep contextualized entity representations with entity-aware self-attention. arXiv:2010.01057. Retrieved from https:\/\/arxiv.org\/abs\/2010.01057","DOI":"10.18653\/v1\/2020.emnlp-main.523"},{"key":"e_1_3_1_233_2","first-page":"28798","article-title":"GraphFormers: GNN-nested transformers for representation learning on textual graph","volume":"34","author":"Yang Junhan","year":"2021","unstructured":"Junhan Yang, Zheng Liu, Shitao Xiao, Chaozhuo Li, Defu Lian, Sanjay Agrawal, Amit Singh, Guangzhong Sun, and Xing Xie. 2021. GraphFormers: GNN-nested transformers for representation learning on textual graph. Advances in Neural Information Processing Systems 34 (2021), 28798\u201328810.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_234_2","unstructured":"Rui Yang Li Fang and Yi Zhou. 2023. CP-KGC: Constrained-prompt knowledge graph completion with large language models. arXiv:2310.08279. Retrieved from https:\/\/arxiv.org\/abs\/2310.08279"},{"key":"e_1_3_1_235_2","unstructured":"Yi Yang Mark Christopher Siy Uy and Allen Huang. 2020. Finbert: A pretrained language model for financial communications. arXiv:2006.08097. Retrieved from https:\/\/arxiv.org\/abs\/2006.08097"},{"key":"e_1_3_1_236_2","unstructured":"Liang Yao Jiazhen Peng Chengsheng Mao and Yuan Luo. 2023. Exploring large language models for knowledge graph completion. arXiv:2308.13916. Retrieved from https:\/\/arxiv.org\/abs\/2308.13916"},{"key":"e_1_3_1_237_2","first-page":"37309","article-title":"Deep bidirectional language-knowledge graph pretraining","volume":"35","author":"Yasunaga Michihiro","year":"2022","unstructured":"Michihiro Yasunaga, Antoine Bosselut, Hongyu Ren, Xikun Zhang, Christopher D. Manning, Percy S. Liang, and Jure Leskovec. 2022. Deep bidirectional language-knowledge graph pretraining. Advances in Neural Information Processing Systems 35 (2022), 37309\u201337323.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_238_2","unstructured":"Ruosong Ye Caiqi Zhang Runhui Wang Shuyuan Xu and Yongfeng Zhang. 2023. Natural language is all a graph needs. arXiv:2308.07134. Retrieved from https:\/\/arxiv.org\/abs\/2308.07134"},{"key":"e_1_3_1_239_2","doi-asserted-by":"publisher","DOI":"10.1145\/3604915.3608874"},{"key":"e_1_3_1_240_2","first-page":"28877","article-title":"Do transformers really perform badly for graph representation","volume":"34","author":"Ying Chengxuan","year":"2021","unstructured":"Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, and Tie-Yan Liu. 2021. Do transformers really perform badly for graph representation? Advances in Neural Information Processing Systems 34 (2021), 28877\u201328888.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_241_2","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219890"},{"key":"e_1_3_1_242_2","first-page":"9240","article-title":"Gnnexplainer: Generating explanations for graph neural networks","volume":"32","author":"Ying Zhitao","year":"2019","unstructured":"Zhitao Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, and Jure Leskovec. 2019. Gnnexplainer: Generating explanations for graph neural networks. Advances in Neural Information Processing Systems 32 (2019), 9240\u20139251.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_243_2","first-page":"5812","article-title":"Graph contrastive learning with augmentations","volume":"33","author":"You Yuning","year":"2020","unstructured":"Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen. 2020. Graph contrastive learning with augmentations. Advances in Neural Information Processing Systems 33 (2020), 5812\u20135823.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_244_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i10.21417"},{"key":"e_1_3_1_245_2","unstructured":"Jianxiang Yu Yuxiang Ren Chenghua Gong Jiaqi Tan Xiang Li and Xuecang Zhang. 2023. Empower text-attributed graphs learning with large language models (LLMs). arXiv:2310.09872. Retrieved from https:\/\/arxiv.org\/abs\/2310.09872"},{"key":"e_1_3_1_246_2","first-page":"11983","article-title":"Graph transformer networks","volume":"32","author":"Yun Seongjun","year":"2019","unstructured":"Seongjun Yun, Minbyul Jeong, Raehyun Kim, Jaewoo Kang, and Hyunwoo J. Kim. 2019. Graph transformer networks. Advances in Neural Information Processing Systems 32 (2019), 11983\u201311993.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_247_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17293"},{"key":"e_1_3_1_248_2","doi-asserted-by":"publisher","DOI":"10.5555\/3600270.3602759"},{"key":"e_1_3_1_249_2","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2024.3369017"},{"key":"e_1_3_1_250_2","unstructured":"Jiawei Zhang. 2023. Graph-ToolFormer: To empower LLMs with graph reasoning ability via prompt augmented by ChatGPT. arXiv:2304.11116. Retrieved from https:\/\/arxiv.org\/abs\/2304.11116"},{"key":"e_1_3_1_251_2","unstructured":"Jiaxing Zhang Jiayi Liu Dongsheng Luo Jennifer Neville and Hua Wei. 2024. LLMExplainer: Large language model based bayesian inference for graph explanation generation. arXiv:2407.15351. Retrieved from https:\/\/arxiv.org\/abs\/2407.15351"},{"key":"e_1_3_1_252_2","unstructured":"Jiahao Zhang Lin Wang Shijie Wang and Wenqi Fan. 2024. Graph unlearning with efficient partial retraining. arXiv:2403.07353. Retrieved from https:\/\/arxiv.org\/abs\/2403.07353"},{"key":"e_1_3_1_253_2","doi-asserted-by":"crossref","unstructured":"Jiahao Zhang Rui Xue Wenqi Fan Xin Xu Qing Li Jian Pei and Xiaorui Liu. 2024. Linear-Time graph neural networks for scalable recommendations. arXiv:2402.13973. Retrieved from https:\/\/arxiv.org\/abs\/2402.13973","DOI":"10.1145\/3589334.3645486"},{"key":"e_1_3_1_254_2","unstructured":"Jiawei Zhang Haopeng Zhang Congying Xia and Li Sun. 2020. Graph-bert: Only attention is needed for learning graph representations. arXiv:2001.05140. Retrieved from https:\/\/arxiv.org\/abs\/2001.05140"},{"key":"e_1_3_1_255_2","unstructured":"Yichi Zhang Zhuo Chen Wen Zhang and Huajun Chen.2023. Making large language models perform better in knowledge graph completion. arXiv:2310.06671. Retrieved from https:\/\/arxiv.org\/abs\/2310.06671"},{"key":"e_1_3_1_256_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2024.3454328"},{"key":"e_1_3_1_257_2","doi-asserted-by":"crossref","unstructured":"Zhengyan Zhang Xu Han Zhiyuan Liu Xin Jiang Maosong Sun and Qun Liu. 2019. ERNIE: Enhanced language representation with informative entities. arXiv:1905.07129. Retrieved from https:\/\/arxiv.org\/abs\/1905.07129","DOI":"10.18653\/v1\/P19-1139"},{"key":"e_1_3_1_258_2","unstructured":"Ziwei Zhang Haoyang Li Zeyang Zhang Yijian Qin Xin Wang and Wenwu Zhu. 2023. Large graph models: A perspective. arXiv:2308.14522. Retrieved from https:\/\/arxiv.org\/abs\/2308.14522"},{"key":"e_1_3_1_259_2","unstructured":"Zhongjian Zhang Xiao Wang Huichi Zhou Yue Yu Mengmei Zhang Cheng Yang and Chuan Shi. 2024. Can large language models improve the adversarial robustness of graph neural networks? arXiv:2408.08685. Retrieved from https:\/\/arxiv.org\/abs\/2408.08685"},{"key":"e_1_3_1_260_2","unstructured":"Zhen Zhang Guanhua Zhang Bairu Hou Wenqi Fan Qing Li Sijia Liu Yang Zhang and Shiyu Chang.2023. Certified robustness for large language models with self-denoising. arXiv:2307.07171. Retrieved from https:\/\/arxiv.org\/abs\/2307.07171"},{"key":"e_1_3_1_261_2","doi-asserted-by":"publisher","DOI":"10.1145\/3639372"},{"key":"e_1_3_1_262_2","doi-asserted-by":"publisher","DOI":"10.1101\/2023.05.30.542904"},{"key":"e_1_3_1_263_2","unstructured":"Jianan Zhao Meng Qu Chaozhuo Li Hao Yan Qian Liu Rui Li Xing Xie and Jian Tang. 2022. Learning on large-scale text-attributed graphs via variational inference. arXiv:2210.14709. Retrieved from https:\/\/arxiv.org\/abs\/2210.14709"},{"key":"e_1_3_1_264_2","unstructured":"Jianan Zhao Le Zhuo Yikang Shen Meng Qu Kai Liu Michael Bronstein Zhaocheng Zhu and Jian Tang. 2023. GraphText: Graph reasoning in text space. arXiv:2310.01089. Retrieved from https:\/\/arxiv.org\/abs\/2310.01089"},{"key":"e_1_3_1_265_2","unstructured":"Qifang Zhao Weidong Ren Tianyu Li Xiaoxiao Xu and Hong Liu. 2023. GraphGPT: Graph learning with generative pre-trained transformers. arXiv:2401.00529. Retrieved from https:\/\/arxiv.org\/abs\/2401.00529"},{"key":"e_1_3_1_266_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2024.3392335"},{"key":"e_1_3_1_267_2","unstructured":"Zihan Zhao Da Ma Lu Chen Liangtai Sun Zihao Li Hongshen Xu Zichen Zhu Su Zhu Shuai Fan Guodong Shen et al. 2024. ChemDFM: Dialogue foundation model for chemistry. arXiv:2401.14818. Retrieved from https:\/\/arxiv.org\/abs\/2401.14818"},{"key":"e_1_3_1_268_2","unstructured":"Yue Zhen Sheng Bi Lu Xing-Tong Pan Wei-Qin Shi Hai-Peng Chen Zi-Rui and Fang Yi-Shu. 2023. Robot task planning based on large language model representing knowledge with directed graph structures. arXiv:2306.05171. Retrieved from https:\/\/arxiv.org\/abs\/2306.05171"},{"key":"e_1_3_1_269_2","first-page":"11458","volume-title":"International Conference on Machine Learning","author":"Zheng Cheng","year":"2020","unstructured":"Cheng Zheng, Bo Zong, Wei Cheng, Dongjin Song, Jingchao Ni, Wenchao Yu, Haifeng Chen, and Wei Wang. 2020. Robust graph representation learning via neural sparsification. In International Conference on Machine Learning. PMLR, 11458\u201311468."},{"key":"e_1_3_1_270_2","unstructured":"Xu Zheng Farhad Shirani Tianchun Wang Wei Cheng Zhuomin Chen Haifeng Chen Hua Wei and Dongsheng Luo. 2023. Towards robust fidelity for evaluating explainability of graph neural networks. arXiv:2310.01820. Retrieved from https:\/\/arxiv.org\/abs\/2310.01820"},{"key":"e_1_3_1_271_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.7005"},{"key":"e_1_3_1_272_2","unstructured":"Jing Zhu Xiang Song Vassilis N. Ioannidis Danai Koutra and Christos Faloutsos. 2023. TouchUp-G: Improving feature representation through graph-centric finetuning. arXiv:2309.13885. Retrieved from https:\/\/arxiv.org\/abs\/2309.13885"},{"key":"e_1_3_1_273_2","doi-asserted-by":"crossref","unstructured":"Yun Zhu Jianhao Guo and Siliang Tang. 2023. SGL-PT: A strong graph learner with graph prompt tuning. arXiv:2302.12449. Retrieved from https:\/\/arxiv.org\/abs\/2302.12449","DOI":"10.2139\/ssrn.4637382"},{"key":"e_1_3_1_274_2","unstructured":"Yinghao Zhu Changyu Ren Shiyun Xie Shukai Liu Hangyuan Ji Zixiang Wang Tao Sun Long He Zhoujun Li Xi Zhu et al. 2024. Realm: Rag-driven enhancement of multimodal electronic health records analysis via large language models. arXiv:2402.07016. Retrieved from https:\/\/arxiv.org\/abs\/2402.07016"},{"key":"e_1_3_1_275_2","unstructured":"Yanqiao Zhu Yichen Xu Feng Yu Qiang Liu Shu Wu and Liang Wang. 2020. Deep graph contrastive representation learning. arXiv:2006.04131. Retrieved from https:\/\/arxiv.org\/abs\/2006.04131"},{"key":"e_1_3_1_276_2","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467314"},{"key":"e_1_3_1_277_2","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220078"}],"container-title":["ACM Transactions on Intelligent Systems and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3732786","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T21:50:17Z","timestamp":1772229017000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3732786"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,18]]},"references-count":276,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2025,10,31]]}},"alternative-id":["10.1145\/3732786"],"URL":"https:\/\/doi.org\/10.1145\/3732786","relation":{},"ISSN":["2157-6904","2157-6912"],"issn-type":[{"value":"2157-6904","type":"print"},{"value":"2157-6912","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,18]]},"assertion":[{"value":"2024-09-07","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-03-18","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-08-18","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}