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Exploring the application of large language models (LLMs) to graph learning is a emerging endeavor.
Ernie: Enhanced language representation with informative entities
Zhengyan Zhang, Xu Han, Zhiyuan Liu, Xin Jiang, Maosong Sun, and Qun Liu. 2019 · 1905
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A note on over-smoothing for graph neural networks
Chen Cai and Yusu Wang. 2020 · 2006
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Sampling from large graphs
Jure Leskovec and Christos Faloutsos. 2006 · 2006
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Unifying the global and local approaches: an efficient power iteration with forward push
Hao Wu, Junhao Gan, Zhewei Wei, and Rui Zhang. 2021 · 2008
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Learning to rank for information retrieval
Tie-Yan Liu et al. 2009 · 2009
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Preserving personalized pagerank in subgraphs
Andrea Vattani, Deepayan Chakrabarti, and Maxim Gurevich. 2011 · 2011
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A brief survey of automatic methods for author name disambiguation
Anderson A Ferreira, Marcos André Gonçalves, and Alberto HF Laender. 2012 · 2012
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Spectral networks and locally connected networks on graphs
Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun. 2013 · 2013
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Principles of random walk , volume 34
Frank Spitzer. 2013 · 2013
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Convolutional neural networks on graphs with fast localized spectral filtering
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst. 2016 · 2016
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling. 2016 · 2016
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A survey of heterogeneous information network analysis
Chuan Shi, Yitong Li, Jiawei Zhang, Yizhou Sun, and S Yu Philip. 2016 · 2016
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Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017 · 2017
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Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017 · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Cited alongside, same era.
Deeper insights into graph convolutional networks for semi-supervised learning
Qimai Li, Zhichao Han, and Xiao-Ming Wu. 2018 · 2018
Cited alongside, same era.
Modeling relational data with graph convolutional networks
Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, and Max Welling. 2018 · 2018
Cited alongside, same era.
Link prediction based on graph neural networks
Muhan Zhang and Yixin Chen. 2018 · 2018
Cited alongside, same era.
Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing
Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, and Aram Galstyan. 2019 · 2019
Cited alongside, same era.
Line graph neural networks for link prediction
Lei Cai, Jundong Li, Jie Wang, and Shuiwang Ji. 2021 · 2021
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Pre-training on large-scale heterogeneous graph
Xunqiang Jiang, Tianrui Jia, Yuan Fang, Chuan Shi, Zhe Lin, and Hui Wang. 2021 · 2021
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Evaluating deep graph neural networks
Wentao Zhang, Zeang Sheng, Yuezihan Jiang, Yikuan Xia, Jun Gao, Zhi Yang, and Bin Cui. 2021 · 2021
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Scaling instruction-finetuned language models
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al. 2022 · 2022
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Efficient and effective training of language and graph neural network models
Vassilis N Ioannidis, Xiang Song, Da Zheng, Houyu Zhang, Jun Ma, Yi Xu, Belinda Zeng, Trishul Chilimbi, and George Karypis. 2022 · 2022
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Scaling graph neural networks with approximate pagerank
Aleksandar Bojchevski, Johannes Gasteiger, Bryan Perozzi, Amol Kapoor, Martin Blais, Benedek Rózemberczki, Michal Lukasik, and Stephan Günnemann. 2020 · 2020
Cited alongside, same era.
Heterogeneous network representation learning
Yuxiao Dong, Ziniu Hu, Kuansan Wang, Yizhou Sun, and Jie Tang. 2020 · 2020
Cited alongside, same era.
Heterogeneous graph transformer
Ziniu Hu, Yuxiao Dong, Kuansan Wang, and Yizhou Sun. 2020 · 2020
Cited alongside, same era.
An analysis framework of research frontiers based on the large-scale open academic graph
Han Huang, Hongyu Wang, and Xiaoguang Wang. 2020 · 2020
Cited alongside, same era.
K-bert: Enabling language representation with knowledge graph
Weijie Liu, Peng Zhou, Zhe Zhao, Zhiruo Wang, Qi Ju, Haotang Deng, and Ping Wang. 2020 · 2020
Cited alongside, same era.
Pre-trained models for natural language processing: A survey
Xipeng Qiu, Tianxiang Sun, Yige Xu, Yunfan Shao, Ning Dai, and Xuanjing Huang. 2020 · 2020
Cited alongside, same era.
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020 · 2020
Cited alongside, same era.
Later among the works it cites.
Learning to rank for information retrieval and natural language processing
Hang Li. 2022 · 2022
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Oag-bert: Towards a unified backbone language model for academic knowledge services
Xiao Liu, Da Yin, Jingnan Zheng, Xingjian Zhang, Peng Zhang, Hongxia Yang, Yuxiao Dong, and Jie Tang. 2022 · 2022
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Learning on large-scale text-attributed graphs via variational inference
Jianan Zhao, Meng Qu, Chaozhuo Li, Hao Yan, Qian Liu, Rui Li, Xing Xie, and Jian Tang. 2022 · 2022
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Talk like a graph: Encoding graphs for large language models
Bahare Fatemi, Jonathan Halcrow, and Bryan Perozzi. 2023 · 2023
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Prodigy: Enabling in-context learning over graphs
Qian Huang, Hongyu Ren, Peng Chen, Gregor Kržmanc, Daniel Zeng, Percy Liang, and Jure Leskovec. 2023 · 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 · 2023
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All in one: Multi-task prompting for graph neural networks
Xiangguo Sun, Hong Cheng, Jia Li, Bo Liu, and Jihong Guan. 2023 · 2023
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Natural language is all a graph needs
Ruosong Ye, Caiqi Zhang, Runhui Wang, Shuyuan Xu, and Yongfeng Zhang. 2023 · 2023
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Heterogeneous graph attention network
Xiao Wang, Houye Ji, Chuan Shi, Bai Wang, Yanfang Ye, Peng Cui, and Philip S Yu. 2019 · 2032
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