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The growing ubiquity of relational data structured as graphs has underscored the need for graph learning models with exceptional generalization capabilities.
Semi-supervised classification with graph convolutional networks
T. N. Kipf and M. Welling · 2017
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Graph attention networks
P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio · 2018
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How powerful are graph neural networks?
K. Xu, W. Hu, J. Leskovec, and S. Jegelka · 2018
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Simplifying graph convolutional networks
F. Wu, A. Souza, T. Zhang, C. Fifty, T. Yu, and K. Weinberger · 2019
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Asgn: An active semi-supervised graph neural network for molecular property prediction
Z. Hao, C. Lu, Z. Huang, H. Wang, Z. Hu, Q. Liu, E. Chen, and C. Lee · 2020
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Lightgcn: Simplifying and powering graph convolution network for recommendation
X. He, K. Deng, X. Wang, Y. Li, Y. Zhang, and M. Wang · 2020
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Heterogeneous graph transformer
Z. Hu, Y. Dong, K. Wang, and Y. Sun · 2020
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Graph structure learning for robust graph neural networks
W. Jin, Y. Ma, X. Liu, X. Tang, S. Wang, and J. Tang · 2020
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Graph contrastive learning with augmentations
Y. You, T. Chen, Y. Sui, T. Chen, Z. Wang, and Y. Shen · 2020
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Sequential recommendation with graph neural networks
J. Chang, C. Gao, Y. Zheng, Y. Hui, Y. Niu, Y. Song, D. Jin, and Y. Li · 2021
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Universal graph convolutional networks
D. Jin, Z. Yu, C. Huo, R. Wang, X. Wang, D. He, and J. Han · 2021
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Training graph neural networks with 1000 layers
G. Li, M. Müller, B. Ghanem, and V. Koltun · 2021
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Learning how to propagate messages in graph neural networks
T. Xiao, Z. Chen, D. Wang, and S. Wang · 2021
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Graph contrastive learning automated
Y. You, T. Chen, Y. Shen, and Z. Wang · 2021
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Graph contrastive learning with adaptive augmentation
Y. Zhu, Y. Xu, F. Yu, Q. Liu, S. Wu, and L. Wang · 2021
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How attentive are graph attention networks?
S. Brody, U. Alon, and E. Yahav · 2022
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Graph unlearning
M. Chen, Z. Zhang, T. Wang, M. Backes, M. Humbert, and Y. Zhang · 2022
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Automated self-supervised learning for graphs
W. Jin, X. Liu, X. Zhao, Y. Ma, N. Shah, and J. Tang · 2022
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Graph self-supervised learning: A survey
Y. Liu, M. Jin, S. Pan, C. Zhou, Y. Zheng, F. Xia, and S. Y. Philip · 2022
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Automated self-supervised learning for recommendation
L. Xia, C. Huang, C. Huang, K. Lin, T. Yu, and B. Kao · 2023
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Cross-domain few-shot graph classification with a reinforced task coordinator
Q. Zhang, S. Pei, Q. Yang, C. Zhang, N. V. Chawla, and X. Zhang · 2023
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Llaga: Large language and graph assistant
R. Chen, T. Zhao, A. Jaiswal, N. Shah, and Z. Wang · 2024
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Position: Relational deep learning-graph representation learning on relational databases
M. Fey, W. Hu, K. Huang, J. E. Lenssen, R. Ranjan, J. Robinson, R. Ying, J. You, and J. Leskovec · 2024
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Zerog: Investigating cross-dataset zero-shot transferability in graphs
Y. Li, P. Wang, Z. Li, J. X. Yu, and J. Li · 2024
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One for all: Towards training one graph model for all classification tasks
H. Liu, J. Feng, L. Kong, N. Liang, D. Tao, Y. Chen, and M. Zhang · 2024
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M. Sun, K. Zhou, X. He, Y. Wang, and X. Wang · 2022
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Omnivl: One foundation model for image-language and video-language tasks
J. Wang, D. Chen, Z. Wu, C. Luo, L. Zhou, Y. Zhao, Y. Xie, C. Liu, Y.-G. Jiang, and L. Yuan · 2022
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Self-supervised learning of graph neural networks: A unified review
Y. Xie, Z. Xu, J. Zhang, Z. Wang, and S. Ji · 2022
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Reproducible scaling laws for contrastive language-image learning
M. Cherti, R. Beaumont, R. Wightman, M. Wortsman, G. Ilharco, C. Gordon, C. Schuhmann, L. Schmidt, and J. Jitsev · 2023
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Universal prompt tuning for graph neural networks
T. Fang, Y. Zhang, Y. Yang, C. Wang, and L. Chen · 2023
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Graphprompt: Unifying pre-training and downstream tasks for graph neural networks
Z. Liu, X. Yu, Y. Fang, and X. Zhang · 2023
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Demystifying structural disparity in graph neural networks: Can one size fit all?
H. Mao, Z. Chen, W. Jin, H. Han, Y. Ma, T. Zhao, N. Shah, and J. Tang · 2024
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Scaling data-constrained language models
N. Muennighoff, A. Rush, B. Barak, T. Le Scao, N. Tazi, A. Piktus, S. Pyysalo, T. Wolf, and C. A. Raffel · 2024
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Graphgpt: Graph instruction tuning for large language models
J. Tang, Y. Yang, W. Wei, L. Shi, L. Su, S. Cheng, D. Yin, and C. Huang · 2024
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Higpt: Heterogeneous graph language model
J. Tang, Y. Yang, W. Wei, L. Shi, L. Xia, D. Yin, and C. Huang · 2024
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Opengraph: Towards open graph foundation models
L. Xia, B. Kao, and C. Huang · 2024
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Language is all a graph needs
R. Ye, C. Zhang, R. Wang, S. Xu, and Y. Zhang · 2024
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All in one and one for all: A simple yet effective method towards cross-domain graph pretraining
H. Zhao, A. Chen, X. Sun, H. Cheng, and J. Li · 2024
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