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Large language models (LLMs) have achieved great success in many fields, and recent works have studied exploring LLMs for graph discriminative tasks such as node classification.
Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules
David Weininger · 1988
Earlier work this paper cites.
Graph convolutional policy network for goal-directed molecular graph generation
Jiaxuan You, Bowen Liu, Zhitao Ying, Vijay Pande, and Jure Leskovec · 2018
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Junction tree variational autoencoder for molecular graph generation
Wengong Jin, Regina Barzilay, and Tommi S. Jaakkola · 2018
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Gnnexplainer: Generating explanations for graph neural networks
Zhitao Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, and Jure Leskovec · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, et al · 2020
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Open graph benchmark: Datasets for machine learning on graphs
Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec · 2020
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Moflow: An invertible flow model for generating molecular graphs
Chengxi Zang and Fei Wang · 2020
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Multi-motifgan (MMGAN): motif-targeted graph generation and prediction
Anuththari Gamage, Eli Chien, Jianhao Peng, and Olgica Milenkovic · 2020
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Mol-cyclegan: a generative model for molecular optimization
Lukasz Maziarka, Agnieszka Pocha, Jan Kaczmarczyk, Krzysztof Rataj, Tomasz Danel, and Michal Warchol · 2020
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Graphgen-redux: a fast and lightweight recurrent model for labeled graph generation
Davide Bacciu and Marco Podda · 2021
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Graphdf: A discrete flow model for molecular graph generation
Youzhi Luo, Keqiang Yan, and Shuiwang Ji · 2021
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On the opportunities and risks of foundation models
Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, et al · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, brian ichter, Fei Xia, Ed Chi, Quoc V Le, and Denny Zhou · 2022
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Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models
Priyan Vaithilingam, Tianyi Zhang, and Elena L. Glassman · 2022
Cited alongside, same era.
A survey on deep graph generation: Methods and applications
Yanqiao Zhu, Yuanqi Du, Yinkai Wang, Yichen Xu, Jieyu Zhang, Qiang Liu, and Shu Wu · 2022
Cited alongside, same era.
Discovering invariant rationales for graph neural networks
Yingxin Wu, Xiang Wang, An Zhang, Xiangnan He, and Tat-Seng Chua · 2022
Cited alongside, same era.
Graph neural architecture search under distribution shifts
Yijian Qin, Xin Wang, Ziwei Zhang, Pengtao Xie, and Wenwu Zhu · 2022
Cited alongside, same era.
A systematic survey on deep generative models for graph generation
Xiaojie Guo and Liang Zhao · 2022
Cited alongside, same era.
Score-based generative modeling of graphs via the system of stochastic differential equations
Kai Sun, Yifan Ethan Xu, Hanwen Zha, Yue Liu, and Xin Luna Dong · 2023
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Graph meets llms: Towards large graph models
Ziwei Zhang, Haoyang Li, Zeyang Zhang, Yijian Qin, Xin Wang, and Wenwu Zhu · 2023
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Natural language is all a graph needs
Ruosong Ye, Caiqi Zhang, Runhui Wang, Shuyuan Xu, and Yongfeng Zhang · 2023
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Can llms effectively leverage graph structural information: When and why
Jin Huang, Xingjian Zhang, Qiaozhu Mei, and Jiaqi Ma · 2023
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Graphgpt: Graph instruction tuning for large language models
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Jaehyeong Jo, Seul Lee, and Sung Ju Hwang · 2022
Cited alongside, same era.
A survey of large language models
Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, et al · 2023
Cited alongside, same era.
Sparks of artificial general intelligence: Early experiments with gpt-4
Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, et al · 2023
Cited alongside, same era.
LEVER: Learning to verify language-to-code generation with execution
Ansong Ni, Srini Iyer, Dragomir Radev, Veselin Stoyanov, Wen-Tau Yih, Sida Wang, and Xi Victoria Lin · 2023
Cited alongside, same era.
LLM drug discovery challenge: A contest as a feasibility study on the utilization of large language models in medicinal chemistry
Kusuri Murakumo, Naruki Yoshikawa, Kentaro Rikimaru, Shogo Nakamura, Kairi Furui, Takamasa Suzuki, Hiroyuki Yamasaki, Yuki Nishigaya, Yuzo Takagi, and Masahito Ohue · 2023
Cited alongside, same era.
Artificial intelligence enabled chatgpt and large language models in drug target discovery, drug discovery, and development
Chiranjib Chakraborty, Manojit Bhattacharya, and Sang-Soo Lee · 2023
Cited alongside, same era.
Llm-assisted knowledge graph engineering: Experiments with chatgpt
Lars-Peter Meyer, Claus Stadler, Johannes Frey, Norman Radtke, Kurt Junghanns, Roy Meissner, Gordian Dziwis, Kirill Bulert, and Michael Martin · 2023
Cited alongside, same era.
Jiabin Tang, Yuhao Yang, Wei Wei, Lei Shi, Lixin Su, Suqi Cheng, Dawei Yin, and Chao Huang · 2023
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Explanations as features: Llm-based features for text-attributed graphs
Xiaoxin He, Xavier Bresson, Thomas Laurent, and Bryan Hooi · 2023
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Exploring the potential of large language models (llms) in learning on graphs
Zhikai Chen, Haitao Mao, Hang Li, Wei Jin, Hongzhi Wen, Xiaochi Wei, Shuaiqiang Wang, Dawei Yin, Wenqi Fan, Hui Liu, and Jiliang Tang · 2023
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Jiayan Guo, Lun Du, and Hengyu Liu · 2023
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Graph-toolformer: To empower llms with graph reasoning ability via prompt augmented by chatgpt
Jiawei Zhang · 2023
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Structgpt: A general framework for large language model to reason over structured data
Jinhao Jiang, Kun Zhou, Zican Dong, Keming Ye, Wayne Xin Zhao, and Ji-Rong Wen · 2023
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Unifying large language models and knowledge graphs: A roadmap
Shirui Pan, Linhao Luo, Yufei Wang, Chen Chen, Jiapu Wang, and Xindong Wu · 2023
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Digress: Discrete denoising diffusion for graph generation
Clément Vignac, Igor Krawczuk, Antoine Siraudin, Bohan Wang, Volkan Cevher, and Pascal Frossard · 2023
Later among the works it cites.