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Large models have emerged as the most recent groundbreaking achievements in artificial intelligence, and particularly machine learning.
Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules
David Weininger · 1988
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Networks
Mark Newman · 2018
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al · 2018
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Learning to represent programs with graphs
Miltiadis Allamanis, Marc Brockschmidt, and Mahmoud Khademi · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Superglue: A stickier benchmark for general-purpose language understanding systems
Alex Wang, Yada Pruksachatkun, Nikita Nangia, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman · 2019
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Using pre-training can improve model robustness and uncertainty
Dan Hendrycks, Kimin Lee, and Mantas Mazeika · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
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Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 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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Deep learning on graphs: A survey
Ziwei Zhang, Peng Cui, and Wenwu Zhu · 2020
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Transformers are graph neural networks
Chaitanya Joshi · 2020
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Sgquant: Squeezing the last bit on graph neural networks with specialized quantization
Boyuan Feng, Yuke Wang, Xu Li, Shu Yang, Xueqiao Peng, and Yufei Ding · 2020
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Degree-quant: Quantization-aware training for graph neural networks
Shyam Anil Tailor, Javier Fernandez-Marques, and Nicholas Donald Lane · 2020
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Mgat: Multimodal graph attention network for recommendation
Zhulin Tao, Yinwei Wei, Xiang Wang, Xiangnan He, Xianglin Huang, and Tat-Seng Chua · 2020
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A compact review of molecular property prediction with graph neural networks
Oliver Wieder, Stefan Kohlbacher, Mélaine Kuenemann, Arthur Garon, Pierre Ducrot, Thomas Seidel, and Thierry Langer · 2020
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Zinc20—a free ultralarge-scale chemical database for ligand discovery
John J Irwin, Khanh G Tang, Jennifer Young, Chinzorig Dandarchuluun, Benjamin R Wong, Munkhzul Khurelbaatar, Yurii S Moroz, John Mayfield, and Roger A Sayle · 2020
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Graph neural networks in particle physics
Jonathan Shlomi, Peter Battaglia, and Jean-Roch Vlimant · 2020
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Neural algorithmic reasoning
Petar Veličković and Charles Blundell · 2021
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Do transformers really perform badly for graph representation?
Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, and Tie-Yan Liu · 2021
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Ogb-lsc: A large-scale challenge for machine learning on graphs
Weihua Hu, Matthias Fey, Hongyu Ren, Maho Nakata, Yuxiao Dong, and Jure Leskovec · 2021
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Self-supervised learning on graphs: Contrastive, generative, or predictive
Lirong Wu, Haitao Lin, Cheng Tan, Zhangyang Gao, and Stan Z Li · 2021
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Computing graph neural networks: A survey from algorithms to accelerators
Sergi Abadal, Akshay Jain, Robert Guirado, Jorge López-Alonso, and Eduard Alarcón · 2021
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Automated machine learning on graphs: A survey
Ziwei Zhang, Xin Wang, and Wenwu Zhu · 2021
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Molgpt: molecular generation using a transformer-decoder model
Viraj Bagal, Rishal Aggarwal, PK Vinod, and U Deva Priyakumar · 2021
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Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Scott Gray, Nick Ryder, Mikhail Pavlov, Alethea Power, Lukasz Kaiser, Mohammad Bavarian, Clemens Winter, Philippe Tillet, Felipe Petroski Such, Dave Cummings, Matthias Plappert, Fotios Chantzis, Elizabeth Barnes, Ariel Herbert-Voss, William Hebgen Guss, Alex Nichol, Alex Paino, Nikolas Tezak, Jie Tang, Igor Babuschkin, Suchir Balaji, Shantanu Jain, William Saunders, Christopher Hesse, Andrew N. Carr, Jan Leike, Josh Achiam, Vedant Misra, Evan Morikawa, Alec Radford, Matthew Knight, Miles Brundage, Mira Murati, Katie Mayer, Peter Welinder, Bob McGrew, Dario Amodei, Sam McCandlish, Ilya Sutskever, and Wojciech Zaremba · 2021
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Graphcode{bert}: Pre-training code representations with data flow
Daya Guo, Shuo Ren, Shuai Lu, Zhangyin Feng, Duyu Tang, Shujie LIU, Long Zhou, Nan Duan, Alexey Svyatkovskiy, Shengyu Fu, Michele Tufano, Shao Kun Deng, Colin Clement, Dawn Drain, Neel Sundaresan, Jian Yin, Daxin Jiang, and Ming Zhou · 2021
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Graph neural network-based fault diagnosis: a review
Zhiwen Chen, Jiamin Xu, Cesare Alippi, Steven X Ding, Yuri Shardt, Tao Peng, and Chunhua Yang · 2021
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A review of graph neural networks and their applications in power systems
Wenlong Liao, Birgitte Bak-Jensen, Jayakrishnan Radhakrishna Pillai, Yuelong Wang, and Yusen Wang · 2021
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High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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Emergent abilities of large language models
Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, Ed H. Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, and William Fedus · 2022
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Few-shot learning on graphs
Chuxu Zhang, Kaize Ding, Jundong Li, Xiangliang Zhang, Yanfang Ye, Nitesh V Chawla, and Huan Liu · 2022
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Multi-task self-supervised graph neural networks enable stronger task generalization
Mingxuan Ju, Tong Zhao, Qianlong Wen, Wenhao Yu, Neil Shah, Yanfang Ye, and Chuxu Zhang · 2022
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Out-of-distribution generalization on graphs: A survey
Haoyang Li, Xin Wang, Ziwei Zhang, and Wenwu Zhu · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
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Explainability in graph neural networks: A taxonomic survey
Hao Yuan, Haiyang Yu, Shurui Gui, and Shuiwang Ji · 2022
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Understanding over-squashing and bottlenecks on graphs via curvature
Jake Topping, Francesco Di Giovanni, Benjamin Paul Chamberlain, Xiaowen Dong, and Michael M Bronstein · 2022
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Transformer for graphs: An overview from architecture perspective
Erxue Min, Runfa Chen, Yatao Bian, Tingyang Xu, Kangfei Zhao, Wenbing Huang, Peilin Zhao, Junzhou Huang, Sophia Ananiadou, and Yu Rong · 2022
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Structure-aware transformer for graph representation learning
Dexiong Chen, Leslie O’Bray, and Karsten Borgwardt · 2022
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Recipe for a general, powerful, scalable graph transformer
Ladislav Rampášek, Michael Galkin, Vijay Prakash Dwivedi, Anh Tuan Luu, Guy Wolf, and Dominique Beaini · 2022
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How expressive are transformers in spectral domain for graphs?
Anson Bastos, Abhishek Nadgeri, Kuldeep Singh, Hiroki Kanezashi, Toyotaro Suzumura, and Isaiah Onando Mulang · 2022
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Pure transformers are powerful graph learners
Jinwoo Kim, Dat Nguyen, Seonwoo Min, Sungjun Cho, Moontae Lee, Honglak Lee, and Seunghoon Hong · 2022
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Graph self-supervised learning: A survey
Yixin Liu, Ming Jin, Shirui Pan, Chuan Zhou, Yu Zheng, Feng Xia, and S Yu Philip · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
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A survey for in-context learning
Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Zhiyong Wu, Baobao Chang, Xu Sun, Jingjing Xu, and Zhifang Sui · 2022
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Gppt: Graph pre-training and prompt tuning to generalize graph neural networks
Mingchen Sun, Kaixiong Zhou, Xin He, Ying Wang, and Xin Wang · 2022
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Bifeat: Supercharge gnn training via graph feature quantization
Yuxin Ma, Ping Gong, Jun Yi, Zhewei Yao, Cheng Li, Yuxiong He, and Feng Yan · 2022
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a 2 q a^{2}q : Aggregation-aware quantization for graph neural networks
Zeyu Zhu, Fanrong Li, Zitao Mo, Qinghao Hu, Gang Li, Zejian Liu, Xiaoyao Liang, and Jian Cheng · 2022
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Graph neural networks in recommender systems: a survey
Shiwen Wu, Fei Sun, Wentao Zhang, Xu Xie, and Bin Cui · 2022
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A systematic survey of molecular pre-trained models
Jun Xia, Yanqiao Zhu, Yuanqi Du, Yue Liu, and Stan Z Li · 2022
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Ultra-dp: Unifying graph pre-training with multi-task graph dual prompt
Mouxiang Chen, Zemin Liu, Chenghao Liu, Jundong Li, Qiheng Mao, and Jianling Sun · 2023
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Prompt tuning for multi-view graph contrastive learning
Chenghua Gong, Xiang Li, Jianxiang Yu, Cheng Yao, Jiaqi Tan, Chengcheng Yu, and Dawei Yin · 2023
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Prompt-based zero- and few-shot node classification: A multimodal approach
Yuexin Li and Bryan Hooi · 2023
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Adaptergnn: Efficient delta tuning improves generalization ability in graph neural networks
Shengrui Li, Xueting Han, and Jing Bai · 2023
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A review on graph neural network methods in financial applications
Jianian Wang, Sheng Zhang, Yanghua Xiao, and Rui Song · 2022
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Competition-level code generation with alphacode
Yujia Li, David Choi, Junyoung Chung, Nate Kushman, Julian Schrittwieser, Rémi Leblond, Tom Eccles, James Keeling, Felix Gimeno, Agustin Dal Lago, Thomas Hubert, Peter Choy, Cyprien de Masson d’Autume, Igor Babuschkin, Xinyun Chen, Po-Sen Huang, Johannes Welbl, Sven Gowal, Alexey Cherepanov, James Molloy, Daniel J. Mankowitz, Esme Sutherland Robson, Pushmeet Kohli, Nando de Freitas, Koray Kavukcuoglu, and Oriol Vinyals · 2022
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Graph neural networks for materials science and chemistry
Patrick Reiser, Marlen Neubert, André Eberhard, Luca Torresi, Chen Zhou, Chen Shao, Houssam Metni, Clint van Hoesel, Henrik Schopmans, Timo Sommer, et al · 2022
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Graph neural networks in network neuroscience
Alaa Bessadok, Mohamed Ali Mahjoub, and Islem Rekik · 2022
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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.
Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C. Berg, Wan-Yen Lo, Piotr Dollár, and Ross Girshick · 2023
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Think-on-graph: Deep and responsible reasoning of large language model with knowledge graph
Jiashuo Sun, Chengjin Xu, Lumingyuan Tang, Saizhuo Wang, Chen Lin, Yeyun Gong, Heung-Yeung Shum, and Jian Guo · 2023
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Anchun Gui, Jinqiang Ye, and Han Xiao · 2023
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Search to fine-tune pre-trained graph neural networks for graph-level tasks
Zhili Wang, Shimin Di, Lei Chen, and Xiaofang Zhou · 2023
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Knowledge distillation on graphs: A survey
Yijun Tian, Shichao Pei, Xiangliang Zhang, Chuxu Zhang, and Nitesh V. Chawla · 2023
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A survey on graph neural network acceleration: Algorithms, systems, and customized hardware
Shichang Zhang, Atefeh Sohrabizadeh, Cheng Wan, Zijie Huang, Ziniu Hu, Yewen Wang, Yingyan, Lin, Jason Cong, and Yizhou Sun · 2023
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Tango: rethinking quantization for graph neural network training on gpus
Shiyang Chen, Da Zheng, Caiwen Ding, Chengying Huan, Yuede Ji, and Hang Liu · 2023
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Vqgraph: Graph vector-quantization for bridging gnns and mlps
Ling Yang, Ye Tian, Minkai Xu, Zhongyi Liu, Shenda Hong, Wei Qu, Wentao Zhang, Bin Cui, Muhan Zhang, and Jure Leskovec · 2023
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Adaptive message quantization and parallelization for distributed full-graph gnn training
Borui Wan, Juntao Zhao, and Chuan Wu · 2023
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Can language models solve graph problems in natural language?
Heng Wang, Shangbin Feng, Tianxing He, Zhaoxuan Tan, Xiaochuang Han, and Yulia Tsvetkov · 2023
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Jiayan Guo, Lun Du, and Hengyu Liu · 2023
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Evaluating large language models on graphs: Performance insights and comparative analysis
Chang Liu and Bo Wu · 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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Natural language is all a graph needs
Ruosong Ye, Caiqi Zhang, Runhui Wang, Shuyuan Xu, and Yongfeng Zhang · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 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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One for all: Towards training one graph model for all classification tasks
Hao Liu, Jiarui Feng, Lecheng Kong, Ningyue Liang, Dacheng Tao, Yixin Chen, and Muhan Zhang · 2023
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Graphtext: Graph reasoning in text space
Jianan Zhao, Le Zhuo, Yikang Shen, Meng Qu, Kai Liu, Michael Bronstein, Zhaocheng Zhu, and Jian Tang · 2023
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Talk like a graph: Encoding graphs for large language models
Bahare Fatemi, Jonathan Halcrow, and Bryan Perozzi · 2023
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Beyond text: A deep dive into large language models’ ability on understanding graph data
Yuntong Hu, Zheng Zhang, and Liang Zhao · 2023
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Graphllm: Boosting graph reasoning ability of large language model
Ziwei Chai, Tianjie Zhang, Liang Wu, Kaiqiao Han, Xiaohai Hu, Xuanwen Huang, and Yang Yang · 2023
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Label-free node classification on graphs with large language models (llms)
Zhikai Chen, Haitao Mao, Hongzhi Wen, Haoyu Han, Wei Jin, Haiyang Zhang, Hui Liu, and Jiliang Tang · 2023
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Empower text-attributed graphs learning with large language models (llms)
Jianxiang Yu, Yuxiang Ren, Chenghua Gong, Jiaqi Tan, Xiang Li, and Xuecang Zhang · 2023
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Pretrained language models to solve graph tasks in natural language
Frederik Wenkel, Guy Wolf, and Boris Knyazev · 2023
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Graphgpt: Graph instruction tuning for large language models
Jiabin Tang, Yuhao Yang, Wei Wei, Lei Shi, Lixin Su, Suqi Cheng, Dawei Yin, and Chao Huang · 2023
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Llm4dyg: Can large language models solve problems on dynamic graphs?
Zeyang Zhang, Xin Wang, Ziwei Zhang, Haoyang Li, Yijian Qin, Simin Wu, and Wenwu Zhu · 2023
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Disentangled representation learning with large language models for text-attributed graphs
Yijian Qin, Xin Wang, Ziwei Zhang, and Wenwu Zhu · 2023
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Gpt-4 doesn’t know it’s wrong: An analysis of iterative prompting for reasoning problems
Kaya Stechly, Matthew Marquez, and Subbarao Kambhampati · 2023
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Graph neural prompting with large language models
Yijun Tian, Huan Song, Zichen Wang, Haozhu Wang, Ziqing Hu, Fang Wang, Nitesh V. Chawla, and Panpan Xu · 2023
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Reasoning on graphs: Faithful and interpretable large language model reasoning
Linhao Luo, Yuan-Fang Li, Gholamreza Haffari, and Shirui Pan · 2023
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A survey on large language models for recommendation
Likang Wu, Zhi Zheng, Zhaopeng Qiu, Hao Wang, Hongchao Gu, Tingjia Shen, Chuan Qin, Chen Zhu, Hengshu Zhu, Qi Liu, et al · 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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Can large language models empower molecular property prediction?
Chen Qian, Huayi Tang, Zhirui Yang, Hong Liang, and Yong Liu · 2023
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Git-mol: A multi-modal large language model for molecular science with graph, image, and text
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Bloomberggpt: A large language model for finance
Shijie Wu, Ozan Irsoy, Steven Lu, Vadim Dabravolski, Mark Dredze, Sebastian Gehrmann, Prabhanjan Kambadur, David Rosenberg, and Gideon Mann · 2023
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Fingpt: Democratizing internet-scale data for financial large language models
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Fingpt: Open-source financial large language models
Hongyang Yang, Xiao-Yang Liu, and Christina Dan Wang · 2023
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Graph neural networks for intelligent transportation systems: A survey
Saeed Rahmani, Asiye Baghbani, Nizar Bouguila, and Zachary Patterson · 2023
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Spatio-temporal graph neural networks for predictive learning in urban computing: A survey
Guangyin Jin, Yuxuan Liang, Yuchen Fang, Jincai Huang, Junbo Zhang, and Yu Zheng · 2023
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Transgpt
Duomo · 2023
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Graph neural networks in iot: a survey
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Ming Jin, Huan Yee Koh, Qingsong Wen, Daniele Zambon, Cesare Alippi, Geoffrey I Webb, Irwin King, and Shirui Pan · 2023
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Scientific discovery in the age of artificial intelligence
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Graph neural networks at the large hadron collider
Gage DeZoort, Peter W Battaglia, Catherine Biscarat, and Jean-Roch Vlimant · 2023
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Combinatorial optimization and reasoning with graph neural networks
Quentin Cappart, Didier Chételat, Elias B Khalil, Andrea Lodi, Christopher Morris, and Petar Velickovic · 2023
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