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In-context learning is the ability of a pretrained model to adapt to novel and diverse downstream tasks by conditioning on prompt examples, without optimizing any parameters.
Semi-supervised classification with graph convolutional networks, 2016
T. N. Kipf and M. Welling · 2016
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Conceptnet 5.5: An open multilingual graph of general knowledge
R. Speer, J. Chin, and C. Havasi · 2017
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Graph attention networks, 2017
P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio · 2017
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One-shot relational learning for knowledge graphs, 2018
W. Xiong, M. Yu, S. Chang, X. Guo, and W. Y. Wang · 2018
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One-shot relational learning for knowledge graphs
W. Xiong, M. Yu, S. Chang, X. Guo, and W. Y. Wang · 2018
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Meta relational learning for few-shot link prediction in knowledge graphs
M. Chen, W. Zhang, W. Zhang, Q. Chen, and H. Chen · 2019
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Bert: Pre-training of deep bidirectional transformers for language understanding
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova · 2019
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Strategies for pre-training graph neural networks
W. Hu, B. Liu, J. Gomes, M. Zitnik, P. Liang, V. Pande, and J. Leskovec · 2019
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Roberta: A robustly optimized BERT pretraining approach
Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer, and V. Stoyanov · 2019
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Language models are few-shot learners
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, et al · 2020
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Open graph benchmark: Datasets for machine learning on graphs
W. Hu, M. Fey, M. Zitnik, Y. Dong, H. Ren, B. Liu, M. Catasta, and J. Leskovec · 2020
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Gpt-gnn: Generative pre-training of graph neural networks
Z. Hu, Y. Dong, K. Wang, K.-W. Chang, and Y. Sun · 2020
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Graph meta learning via local subgraphs, 2020
K. Huang and M. Zitnik · 2020
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Gcc: Graph contrastive coding for graph neural network pre-training
J. Qiu, Q. Chen, Y. Dong, J. Zhang, H. Yang, M. Ding, K. Wang, and J. Tang · 2020
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MPNet: Masked and Permuted Pre-training for Language Understanding
Few-shot knowledge graph completion
C. Zhang, H. Yao, C. Huang, M. Jiang, Z. Li, and N. V. Chawla · 2020
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Ogb-lsc: A large-scale challenge for machine learning on graphs
W. Hu, M. Fey, H. Ren, M. Nakata, Y. Dong, and J. Leskovec · 2021
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Identifying possible rumor spreaders on twitter: A weak supervised learning approach
S. Sharma and R. Sharma · 2021
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One-shot relation learning for knowledge graphs via neighborhood aggregation and paths encoding
J. Sun, Y. Zhou, and C. Zong · 2021
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Relational multi-task learning: Modeling relations between data and tasks
K. Cao, J. You, and J. Leskovec · 2022
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K. Song, X. Tan, T. Qin, J. Lu, and T.-Y. Liu · 2020
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Graph neural networks in recommender systems: A survey
S. Wu, W. Zhang, F. Sun, and B. Cui · 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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Q. Huang, H. Ren, and J. Leskovec · 2022
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Task-adaptive few-shot node classification
S. Wang, K. Ding, C. Zhang, C. Chen, and J. Li · 2022
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Task-adaptive few-shot node classification
S. Wang, K. Ding, C. Zhang, C. Chen, and J. Li · 2022
Later among the works it cites.