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Large language models~(LLM) like ChatGPT have become indispensable to artificial general intelligence~(AGI), demonstrating excellent performance in various natural language processing tasks.
Dropedge: Towards deep graph convolutional networks on node classification
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Tablegpt: Few-shot table-to-text generation with table structure reconstruction and content matching
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A measure of betweenness centrality based on random walks
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Arnetminer: extraction and mining of academic social networks
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Graph markup language (graphml)
Ulrik Brandes, Markus Eiglsperger, Jürgen Lerner, and Christian Pich. 2013 · 2013
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Recommendation systems: Principles, methods and evaluation
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling. 2016 · 2016
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Graph attention networks
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, Yoshua Bengio, et al. 2017 · 2017
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Degree centrality, betweenness centrality, and closeness centrality in social network
Junlong Zhang and Yu Luo. 2017 · 2017
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Variational reasoning for question answering with knowledge graph
Yuyu Zhang, Hanjun Dai, Zornitsa Kozareva, Alexander Smola, and Le Song. 2018 · 2018
Finetuned language models are zero-shot learners
Jason Wei, Maarten Bosma, Vincent Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, and Quoc V Le. 2021 · 2021
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Data augmentation for graph neural networks
Tong Zhao, Yozen Liu, Leonardo Neves, Oliver Woodford, Meng Jiang, and Neil Shah. 2021 · 2021
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Towards table-to-text generation with pretrained language model: A table structure understanding and text deliberating approach
Miao Chen, Xinjiang Lu, Tong Xu, Yanyan Li, Zhou Jingbo, Dejing Dou, and Hui Xiong. 2022 · 2022
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Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022 · 2022
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PLOG: Table-to-logic pretraining for logical table-to-text generation
Ao Liu, Haoyu Dong, Naoaki Okazaki, Shi Han, and Dongmei Zhang. 2022 · 2022
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Knowledge graph embedding based question answering
Xiao Huang, Jingyuan Zhang, Dingcheng Li, and Ping Li. 2019 · 2019
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Rethinking table recognition using graph neural networks
Shah Rukh Qasim, Hassan Mahmood, and Faisal Shafait. 2019 · 2019
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Bioinformatics
Andreas D Baxevanis, Gary D Bader, and David S Wishart. 2020 · 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 · 2020
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Applications of deep learning in molecule generation and molecular property prediction
W Patrick Walters and Regina Barzilay. 2020 · 2020
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Mate: multi-view attention for table transformer efficiency
Julian Martin Eisenschlos, Maharshi Gor, Thomas Müller, and William W Cohen. 2021 · 2021
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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 · 2022
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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 · 2023
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Toolformer: Language models can teach themselves to use tools
Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom. 2023 · 2023
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Yuan Sui, Mengyu Zhou, Mingjie Zhou, Shi Han, and Dongmei Zhang. 2023 · 2023
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Tree of thoughts: Deliberate problem solving with large language models
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L Griffiths, Yuan Cao, and Karthik Narasimhan. 2023 · 2023
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Graph-toolformer: To empower llms with graph reasoning ability via prompt augmented by chatgpt
Jiawei Zhang. 2023 · 2023
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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 · 2023
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