{"type":"ScholarlyArticle","title":"A Survey of Graph Meets Large Language Model: Progress and Future Directions","authors":[{"name":"Yuhan Li","slug":"yuhan-li","given":"Yuhan","family":"Li","affiliation":"The Hong Kong University of Science and Technology (Guangzhou )"},{"name":"Zhixun Li","slug":"zhixun-li","given":"Zhixun","family":"Li","affiliation":"SHIAE and SEEM , The Chinese University of Hong Kong"},{"name":"Peisong Wang","slug":"peisong-wang","given":"Peisong","family":"Wang","affiliation":"Tsinghua University"},{"name":"Jia Li","slug":"jia-li","given":"Jia","family":"Li","affiliation":"The Hong Kong University of Science and Technology (Guangzhou )"},{"name":"Xiangguo Sun","slug":"xiangguo-sun","given":"Xiangguo","family":"Sun","affiliation":"SHIAE and SEEM , The Chinese University of Hong Kong"},{"name":"Hong Cheng","slug":"hong-cheng","given":"Hong","family":"Cheng","affiliation":"SHIAE and SEEM , The Chinese University of Hong Kong"},{"name":"Jeffrey Xu Yu","slug":"jeffrey-xu-yu","given":"Jeffrey Xu","family":"Yu","affiliation":""}],"abstract":"Graph plays a significant role in representing and analyzing complex relationships in real-world applications such as citation networks, social networks, and biological data. Recently, Large Language Models (LLMs), which have achieved tremendous success in various domains, have also been leveraged in graph-related tasks to surpass traditional Graph Neural Networks (GNNs) based methods and yield state-of-the-art performance. In this survey, we first present a comprehensive review and analysis of existing methods that integrate LLMs with graphs. First of all, we propose a new taxonomy, which organizes existing methods into three categories based on the role (i.e., enhancer, predictor, and alignment component) played by LLMs in graph-related tasks. Then we systematically survey the representative methods along the three categories of the taxonomy. Finally, we discuss the remaining limitations of existing studies and highlight promising avenues for future research. The relevant papers are summarized and will be consistently updated at: https://github.com/yhLeeee/Awesome-LLMs-in-Graph-tasks.","doi":"10.24963/ijcai.2024/898","url":"https://ijcai.org/proceedings/2024/898","pdf":"https://ijcai.org/proceedings/2024/0898.pdf","markdown":"https://ijcai.org/proceedings/2024/898.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/898","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Survey Track","track":"Survey Track","number":898,"pages":{"first":8123,"last":8131},"keywords":["Data Mining: DM: Mining graphs","Natural Language Processing: NLP: Applications"],"videos":[],"references":[{"position":1,"raw":"[Brannon et al., 2023] William Brannon, Suyash Fulay, et al. Con- grat: Self-supervised contrastive pretraining for joint graph and text embeddings. arXiv preprint arXiv:2305.14321, 2023.","title":"Congrat: Self-supervised contrastive pretraining for joint graph and text embeddings","authors":"William Brannon, Suyash Fulay","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Brown et al., 2020] Tom Brown, Benjamin Mann, et al. Language models are few-shot learners. NeurIPS, 33:1877-1901, 2020.","title":"Language models are few-shot learners","authors":"Tom Brown, Benjamin Mann","year":"2020","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Cao et al., 2023] He Cao, Zijing Liu, et al. Instructmol: Multi- modal integration for building a versatile and reliable molecular assistant in drug discovery. arXiv preprint arXiv:2311.16208, 2023.","title":"Instructmol: Multimodal integration for building a versatile and reliable molecular assistant in drug discovery","authors":"Cao","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Chai et al., 2023] Ziwei Chai, Tianjie Zhang, et al. Graphllm: Boosting graph reasoning ability of large language model. arXiv preprint arXiv:2310.05845, 2023.","title":"Graphllm: Boosting graph reasoning ability of large language model","authors":"Ziwei Chai, Tianjie Zhang","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Chandra et al., 2020] Shantanu Chandra, Pushkar Mishra, et al. Graph-based modeling of online communities for fake news de- tection. arXiv preprint arXiv:2008.06274, 2020.","title":"Graph-based modeling of online communities for fake news detection","authors":"Shantanu Chandra, Pushkar Mishra","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Chen et al., 2023] Zhikai Chen, Haitao Mao, et al. Exploring the potential of large language models (llms) in learning on graphs. arXiv preprint arXiv:2307.03393, 2023.","title":"Exploring the potential of large language models (llms) in learning on graphs","authors":"Chen","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Chen et al., 2024] Zhikai Chen, Haitao Mao, et al. Label-free node classification on graphs with large language models (llms). In ICLR, 2024.","title":"Label-free node classification on graphs with large language models (llms)","authors":"Chen","year":"2024","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Chien et al., 2021] Eli Chien, Wei-Cheng Chang, et al. Node fea- ture extraction by self-supervised multi-scale neighborhood pre- diction. arXiv preprint arXiv:2111.00064, 2021.","title":"Node feature extraction by self-supervised multi-scale neighborhood prediction","authors":"Eli Chien, Wei-Cheng Chang","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Chowdhery et al., 2022] Aakanksha Chowdhery, Sharan Narang, et al. Palm: Scaling language modeling with pathways. arXiv preprint arXiv:2204.02311, 2022.","title":"Palm: Scaling language modeling with pathways","authors":"Aakanksha Chowdhery, Sharan Narang","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Das et al., 2023] Debarati Das, Ishaan Gupta, et al. Which modality should i use-text, motif, or image?: Understanding graphs with large language models. arXiv preprint arXiv:2311.09862, 2023.","title":"Which modality should i use-text, motif, or image?: Understanding graphs with large language models","authors":"Debarati Das, Ishaan Gupta","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Duan et al., 2023] Keyu Duan, Qian Liu, et al. Simteg: A frustrat- ingly simple approach improves textual graph learning. arXiv preprint arXiv:2308.02565, 2023.","title":"Simteg: A frustratingly simple approach improves textual graph learning","authors":"Qian Keyu Duan, Liu","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Dwivedi et al., 2022] Vijay Prakash Dwivedi, Ladislav Rampášek, et al. Long range graph benchmark. NeurIPS, 35:22326-22340, 2022.","title":"Long range graph benchmark","authors":"Vijay Prakash Dwivedi, Ladislav Rampášek","year":"2022","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Edwards et al., 2021] Carl Edwards, ChengXiang Zhai, et al. Text2mol: Cross-modal molecule retrieval with natural language queries. In EMNLP, pages 595-607, 2021.","title":"Text2mol: Cross-modal molecule retrieval with natural language queries","authors":"Carl Edwards, Chengxiang Zhai","year":"2021","venue":"EMNLP","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Fatemi et al., 2023] Bahare Fatemi, Jonathan Halcrow, et al. Talk like a graph: Encoding graphs for large language models. arXiv preprint arXiv:2310.04560, 2023.","title":"Talk like a graph: Encoding graphs for large language models","authors":"Fatemi","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Guo et al., 2023] Jiayan Guo, Lun Du, et al. Gpt4graph: Can large language models understand graph structured data? an empirical evaluation and benchmarking. arXiv preprint arXiv:2305.15066, 2023.","title":"Gpt4graph: Can large language models understand graph structured data? an empirical evaluation and benchmarking","authors":"Jiayan Guo, Lun Du","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Hamilton et al., 2017] Will Hamilton, Zhitao Ying, et al. Inductive representation learning on large graphs. NeurIPS, 30, 2017.","title":"Inductive representation learning on large graphs","authors":"Hamilton","year":"2017","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[He et al., 2024] Xiaoxin He, Xavier Bresson, et al. Explanations as features: Llm-based features for text-attributed graphs. In ICLR, 2024.","title":"Explanations as features: Llm-based features for text-attributed graphs","authors":"He","year":"2024","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Hu et al., 2021] Edward J Hu, Phillip Wallis, et al. Lora: Low-rank adaptation of large language models. In ICLR, 2021.","title":"Lora: Low-rank adaptation of large language models","authors":"J Edward, Phillip Hu, Wallis","year":"2021","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Hu et al., 2023] Yuntong Hu, Zheng Zhang, et al. Beyond text: A deep dive into large language models' ability on understanding graph data. arXiv preprint arXiv:2310.04944, 2023.","title":"Beyond text: A deep dive into large language models' ability on understanding graph data","authors":"Yuntong Hu, Zheng Zhang","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Huang et al., 2023a] Jin Huang, Xingjian Zhang, et al. Can llms effectively leverage graph structural information: when and why. arXiv preprint arXiv:2309.16595, 2023.","title":"Can llms effectively leverage graph structural information: when and why","authors":"Huang","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Huang et al., 2023b] Xuanwen Huang, Kaiqiao Han, et al. Prompt- based node feature extractor for few-shot learning on text- attributed graphs. arXiv preprint arXiv:2309.02848, 2023.","title":"Promptbased node feature extractor for few-shot learning on textattributed graphs","authors":"Huang","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Ji et al., 2021] Shaoxiong Ji, Shirui Pan, et al. A survey on knowledge graphs: Representation, acquisition, and applications. TNNLS, 33(2):494-514, 2021.","title":"Shaoxiong Ji, Shirui Pan, et al. A survey on knowledge graphs: Representation, acquisition, and applications","authors":"Ji","year":"2021","venue":"TNNLS","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Jin et al., 2023a] Bowen Jin, Wentao Zhang, et al. Learning mul- tiplex embeddings on text-rich networks with one text encoder. arXiv preprint arXiv:2310.06684, 2023.","title":"Learning multiplex embeddings on text-rich networks with one text encoder","authors":"Jin","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Jin et al., 2023b] Bowen Jin, Wentao Zhang, et al. Patton: Lan- guage model pretraining on text-rich networks. arXiv preprint arXiv:2305.12268, 2023.","title":"Patton: Language model pretraining on text-rich networks","authors":"Jin","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Kenton and others, 2019] Jacob Devlin Ming-Wei Chang Kenton et al. Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of NAACL-HLT, pages 4171-4186, 2019.","title":"Bert: Pre-training of deep bidirectional transformers for language understanding","authors":"Jacob Devlin, Ming-Wei Chang, Kenton","year":"2019","venue":"Proceedings of NAACL-HLT","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Kipf and Welling, 2016] Thomas N Kipf and Max Welling. Semi- supervised classification with graph convolutional networks. In ICLR, 2016.","title":"Semisupervised classification with graph convolutional networks","authors":"N Thomas, Max Kipf, Welling","year":"2016","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Li and Liang, 2021] Xiang Lisa Li and Percy Liang. Prefix-tuning: Optimizing continuous prompts for generation. In ACL, pages 4582-4597, 2021.","title":"Prefix-tuning: Optimizing continuous prompts for generation","authors":"Lisa Xiang, Percy Li, Liang","year":"2021","venue":"ACL","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Li et al., 2023a] Junnan Li, Dongxu Li, et al. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. arXiv preprint arXiv:2301.12597, 2023.","title":"Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models","authors":"Li","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Li et al., 2023b] Yichuan Li, Kaize Ding, et al. Grenade: Graph- centric language model for self-supervised representation learning on text-attributed graphs. arXiv preprint arXiv:2310.15109, 2023.","title":"Grenade: Graphcentric language model for self-supervised representation learning on text-attributed graphs","authors":"Li","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Liu and Wu, 2023] Chang Liu and Bo Wu. Evaluating large lan- guage models on graphs: Performance insights and comparative analysis. arXiv preprint arXiv:2308.11224, 2023.","title":"Evaluating large language models on graphs: Performance insights and comparative analysis","authors":"Chang Liu, Bo Wu","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Liu et al., 2022] Shengchao Liu, Weili Nie, et al. Multi-modal molecule structure-text model for text-based retrieval and editing. arXiv preprint arXiv:2212.10789, 2022.","title":"Multi-modal molecule structure-text model for text-based retrieval and editing","authors":"Liu","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Liu et al., 2023a] Jiawei Liu, Cheng Yang, et al. Towards graph foundation models: A survey and beyond. arXiv preprint arXiv:2310.11829, 2023.","title":"Towards graph foundation models: A survey and beyond","authors":"Liu","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Liu et al., 2023b] Pengfei Liu, Yiming Ren, et al. Git-mol: A multi- modal large language model for molecular science with graph, image, and text. arXiv preprint arXiv:2308.06911, 2023.","title":"Git-mol: A multimodal large language model for molecular science with graph, image","authors":"Liu","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Liu et al., 2023c] Zhiyuan Liu, Sihang Li, et al. Molca: Molecular graph-language modeling with cross-modal projector and uni- modal adapter. arXiv preprint arXiv:2310.12798, 2023.","title":"Molca: Molecular graph-language modeling with cross-modal projector and unimodal adapter","authors":"Liu","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Liu et al., 2024] Hao Liu, Jiarui Feng, et al. One for all: Towards training one graph model for all classification tasks. In ICLR, 2024.","title":"One for all: Towards training one graph model for all classification tasks","authors":"Hao Liu, Jiarui Feng","year":"2024","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Mavromatis et al., 2023] Costas Mavromatis, Vassilis N Ioannidis, et al. Train your own gnn teacher: Graph-aware distillation on textual graphs. arXiv preprint arXiv:2304.10668, 2023.","title":"Train your own gnn teacher: Graph-aware distillation on textual graphs","authors":"Costas Mavromatis, Vassilis N Ioannidis","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Mikolov et al., 2013] Tomas Mikolov, Ilya Sutskever, et al. Dis- tributed representations of words and phrases and their composi- tionality. NeurIPS, 26, 2013.","title":"Tomas Mikolov, Ilya Sutskever, et al. Distributed representations of words and phrases and their compositionality","authors":"Mikolov","year":"2013","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Oord et al., 2018] Aaron van den Oord, Yazhe Li, et al. Represen- tation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748, 2018. [OpenAI, 2023] OpenAI. Gpt-4 technical report, 2023.","title":"Representation learning with contrastive predictive coding","authors":"Aaron Van Den Oord, Yazhe Li","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Qian et al., 2023] Chen Qian, Huayi Tang, et al. Can large language models empower molecular property prediction? arXiv preprint arXiv:2307.07443, 2023.","title":"Can large language models empower molecular property prediction","authors":"Chen Qian, Huayi Tang","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Qin et al., 2023] Yijian Qin, Xin Wang, et al. Disentangled repre- sentation learning with large language models for text-attributed graphs. arXiv preprint arXiv:2310.18152, 2023.","title":"Disentangled representation learning with large language models for text-attributed graphs","authors":"Qin","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Radford et al., 2021] Alec Radford, Jong Wook Kim, et al. Learn- ing transferable visual models from natural language supervision. In ICLR, pages 8748-8763, 2021.","title":"Learning transferable visual models from natural language supervision","authors":"Alec Radford, Jong Wook Kim","year":"2021","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Ren et al., 2023] Xubin Ren, Wei Wei, et al. Representation learn- ing with large language models for recommendation. arXiv preprint arXiv:2310.15950, 2023.","title":"Representation learning with large language models for recommendation","authors":"Ren","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Salton and Buckley, 1988] Gerard Salton and Christopher Buck- ley. Term-weighting approaches in automatic text retrieval. IPM, 24(5):513-523, 1988.","title":"Term-weighting approaches in automatic text retrieval","authors":"Buckley Salton, Christopher Buckley","year":"1988","venue":"IPM","doi":null,"url":null,"internal_url":null},{"position":44,"raw":"[Sen et al., 2008] Prithviraj Sen, Galileo Namata, et al. Collective classification in network data. AI magazine, 29(3):93-93, 2008.","title":"Collective classification in network data","authors":"Prithviraj Sen, Galileo Namata","year":"2008","venue":"AI magazine","doi":null,"url":null,"internal_url":null},{"position":45,"raw":"[Shayegani et al., 2023] Erfan Shayegani, Md Abdullah Al Mamun, et al. Survey of vulnerabilities in large language models revealed by adversarial attacks. arXiv preprint arXiv:2310.10844, 2023.","title":"Survey of vulnerabilities in large language models revealed by adversarial attacks","authors":"Erfan Shayegani, Md Abdullah Al Mamun","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":46,"raw":"[Shi et al., 2023] Yaorui Shi, An Zhang, et al. Relm: Leveraging language models for enhanced chemical reaction prediction. arXiv preprint arXiv:2310.13590, 2023.","title":"Relm: Leveraging language models for enhanced chemical reaction prediction","authors":"Shi","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":47,"raw":"[Song et al., 2020] Chao Song, Youfang Lin, et al. Spatial-temporal synchronous graph convolutional networks: A new framework for spatial-temporal network data forecasting. In AAAI, pages 914-921, 2020.","title":"Spatial-temporal synchronous graph convolutional networks: A new framework for spatial-temporal network data forecasting","authors":"Chao Song, Youfang Lin","year":"2020","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":48,"raw":"[Su et al., 2022] Bing Su, Dazhao Du, et al. A molecular multi- modal foundation model associating molecule graphs with natural language. arXiv preprint arXiv:2209.05481, 2022.","title":"A molecular multimodal foundation model associating molecule graphs with natural language","authors":"Su","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":49,"raw":"[Tan et al., 2023] Yanchao Tan, Zihao Zhou, et al. Walklm: A uni- form language model fine-tuning framework for attributed graph embedding. In NeurIPS, 2023.","title":"Walklm: A uniform language model fine-tuning framework for attributed graph embedding","authors":"Tan","year":"2023","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":50,"raw":"[Tang et al., 2023] Jiabin Tang, Yuhao Yang, et al. Graphgpt: Graph instruction tuning for large language models. arXiv preprint arXiv:2310.13023, 2023.","title":"Graphgpt: Graph instruction tuning for large language models","authors":"Jiabin Tang, Yuhao Yang","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":51,"raw":"[Vaswani et al., 2017] Ashish Vaswani, Noam Shazeer, et al. Atten- tion is all you need. NeurIPS, 30, 2017.","title":"Attention is all you need","authors":"Ashish Vaswani, Noam Shazeer","year":"2017","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":52,"raw":"[Velickovic et al., 2018] Petar Velickovic, Guillem Cucurull, et al. Graph attention networks. stat, 1050:4, 2018.","title":"Graph attention networks","authors":"Petar Velickovic, Guillem Cucurull","year":"2018","venue":"stat","doi":null,"url":null,"internal_url":null},{"position":53,"raw":"[Wang et al., 2023a] Haishuai Wang, Yang Gao, et al. Graph neural architecture search with gpt-4. arXiv preprint arXiv:2310.01436, 2023.","title":"Graph neural architecture search with gpt-4","authors":"Wang","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":54,"raw":"[Wang et al., 2023b] Heng Wang, Shangbin Feng, et al. Can lan- guage models solve graph problems in natural language? arXiv preprint arXiv:2305.10037, 2023.","title":"Can language models solve graph problems in natural language","authors":"Wang","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":55,"raw":"[Wei et al., 2022a] Jason Wei, Yi Tay, et al. Emergent abilities of large language models. TMLR, 2022.","title":"Emergent abilities of large language models","authors":"Wei","year":"2022","venue":"TMLR","doi":null,"url":null,"internal_url":null},{"position":56,"raw":"[Wei et al., 2022b] Jason Wei, Xuezhi Wang, et al. Chain-of-thought prompting elicits reasoning in large language models. NeurIPS, 35:24824-24837, 2022.","title":"Chain-of-thought prompting elicits reasoning in large language models","authors":"Wei","year":"2022","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":57,"raw":"[Wei et al., 2023] Wei Wei, Xubin Ren, et al. Llmrec: Large lan- guage models with graph augmentation for recommendation. arXiv preprint arXiv:2311.00423, 2023.","title":"Llmrec: Large language models with graph augmentation for recommendation","authors":"Wei Wei, Xubin Ren","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":58,"raw":"[Wen and Fang, 2023] Zhihao Wen and Yuan Fang. Prompt tun- ing on graph-augmented low-resource text classification. arXiv preprint arXiv:2307.10230, 2023.","title":"Prompt tuning on graph-augmented low-resource text classification","authors":"Zhihao Wen, Yuan Fang","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":59,"raw":"[Wu et al., 2018] Zhenqin Wu, Bharath Ramsundar, et al. Molecu- lenet: a benchmark for molecular machine learning. Chemical science, 9(2):513-530, 2018.","title":"Moleculenet: a benchmark for molecular machine learning","authors":"Wu","year":"2018","venue":"Chemical science","doi":null,"url":null,"internal_url":null},{"position":60,"raw":"[Xie et al., 2023] Han Xie, Da Zheng, et al. Graph-aware language model pre-training on a large graph corpus can help multiple graph applications. arXiv preprint arXiv:2306.02592, 2023.","title":"Graph-aware language model pre-training on a large graph corpus can help multiple graph applications","authors":"Han Xie, Da Zheng","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":61,"raw":"[Xu et al., 2018] Keyulu Xu, Weihua Hu, et al. How powerful are graph neural networks? arXiv preprint arXiv:1810.00826, 2018.","title":"How powerful are graph neural networks","authors":"Xu","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":62,"raw":"[Xue et al., 2023] Rui Xue, Xipeng Shen, et al. Efficient large language models fine-tuning on graphs. arXiv preprint arXiv:2312.04737, 2023.","title":"Efficient large language models fine-tuning on graphs","authors":"Rui Xue, Xipeng Shen","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":63,"raw":"[Yang et al., 2021] Junhan Yang, Zheng Liu, et al. Graphformers: Gnn-nested transformers for representation learning on textual graph. NeurIPS, 34:28798-28810, 2021.","title":"Graphformers: Gnn-nested transformers for representation learning on textual graph","authors":"Yang","year":"2021","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":64,"raw":"[Yang et al., 2023] Jingfeng Yang, Hongye Jin, et al. Harnessing the power of llms in practice: A survey on chatgpt and beyond. arXiv preprint arXiv:2304.13712, 2023.","title":"Harnessing the power of llms in practice: A survey on chatgpt and beyond","authors":"Jingfeng Yang, Hongye Jin","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":65,"raw":"[Ye et al., 2023] Ruosong Ye, Caiqi Zhang, Runhui Wang, Shuyuan Xu, and Yongfeng Zhang. Natural language is all a graph needs. arXiv preprint arXiv:2308.07134, 2023.","title":"Natural language is all a graph needs","authors":"Ye","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":66,"raw":"[Yu et al., 2023] Jianxiang Yu, Yuxiang Ren, et al. Empower text- attributed graphs learning with large language models (llms). arXiv preprint arXiv:2310.09872, 2023.","title":"Empower textattributed graphs learning with large language models","authors":"Yu","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":67,"raw":"[Zhang et al., 2023a] Zeyang Zhang, Xin Wang, et al. Llm4dyg: Can large language models solve problems on dynamic graphs? arXiv preprint arXiv:2310.17110, 2023.","title":"Llm4dyg: Can large language models solve problems on dynamic graphs","authors":"Zhang","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":68,"raw":"[Zhang et al., 2023b] Ziwei Zhang, Haoyang Li, et al. Large graph models: A perspective. arXiv preprint arXiv:2308.14522, 2023.","title":"Large graph models: A perspective","authors":"Zhang","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":69,"raw":"[Zhao et al., 2022] Jianan Zhao, Meng Qu, et al. Learning on large-scale text-attributed graphs via variational inference. arXiv preprint arXiv:2210.14709, 2022.","title":"Learning on large-scale text-attributed graphs via variational inference","authors":"Jianan Zhao, Meng Qu","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":70,"raw":"[Zhao et al., 2023a] Haiteng Zhao, Shengchao Liu, et al. Gimlet: A unified graph-text model for instruction-based molecule zero-shot learning. arXiv preprint arXiv:2306.13089, 2023.","title":"Gimlet: A unified graph-text model for instruction-based molecule zero-shot learning","authors":"Zhao","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":71,"raw":"[Zhao et al., 2023b] Jianan Zhao, Le Zhuo, et al. Graphtext: Graph reasoning in text space. arXiv preprint arXiv:2310.01089, 2023.","title":"Graphtext: Graph reasoning in text space","authors":"Zhao","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":72,"raw":"[Zhao et al., 2023c] Wayne Xin Zhao, Kun Zhou, et al. A survey of large language models. arXiv preprint arXiv:2303.18223, 2023.","title":"A survey of large language models","authors":"Zhao","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":73,"raw":"[Zhu et al., 2023] Jing Zhu, Xiang Song, et al. Touchup-g: Improv- ing feature representation through graph-centric finetuning. arXiv preprint arXiv:2309.13885, 2023.","title":"Touchup-g: Improving feature representation through graph-centric finetuning","authors":"Jing Zhu, Xiang Song","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":74,"raw":"[Zou et al., 2023] Tao Zou, Le Yu, et al. Pretraining language mod- els with text-attributed heterogeneous graphs. arXiv preprint arXiv:2310.12580, 2023.","title":"Pretraining language models with text-attributed heterogeneous graphs","authors":"Tao Zou, Le Yu","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}