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In recent years, large language models (LLMs) have demonstrated remarkable generalization capabilities across various natural language processing (NLP) tasks.
Collective classification in network data
P. Sen, G. Namata, M. Bilgic, L. Getoor, B. Galligher, and T. Eliassi-Rad · 2008
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Information network or social network? the structure of the twitter follow graph
S. A. Myers, A. Sharma, P. Gupta, and J. Lin · 2014
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Observed versus latent features for knowledge base and text inference
K. Toutanova and D. Chen · 2015
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D. Ha, A. Dai, and Q. V. Le · 2016
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Semi-supervised classification with graph convolutional networks
T. N. Kipf and M. Welling · 2016
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Revisiting semi-supervised learning with graph embeddings
Z. Yang, W. Cohen, and R. Salakhudinov · 2016
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P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio · 2017
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Modeling relational data with graph convolutional networks
M. Schlichtkrull, T. N. Kipf, P. Bloem, R. Van Den Berg, I. Titov, and M. Welling · 2018
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Pitfalls of graph neural network evaluation
O. Shchur, M. Mumme, A. Bojchevski, and S. Günnemann · 2018
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P. Veličković, W. Fedus, W. L. Hamilton, P. Liò, Y. Bengio, and R. D. Hjelm · 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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Sentence-bert: Sentence embeddings using siamese bert-networks
N. Reimers · 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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Geom-gcn: Geometric graph convolutional networks
H. Pei, B. Wei, K. C.-C. Chang, Y. Lei, and B. Yang · 2020
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A comprehensive survey on graph neural networks
Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and S. Y. Philip · 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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Node feature extraction by self-supervised multi-scale neighborhood prediction
E. Chien, W.-C. Chang, C.-J. Hsieh, H.-F. Yu, J. Zhang, O. Milenkovic, and I. S. Dhillon · 2021
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Abcd: A graph framework to convert complex sentences to a covering set of simple sentences
Y. Gao, T.-H. Huang, and R. J. Passonneau · 2021
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Large-scale representation learning on graphs via bootstrapping
S. Thakoor, C. Tallec, M. G. Azar, M. Azabou, E. L. Dyer, R. Munos, P. Veličković, and M. Valko · 2021
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Graphmae: Self-supervised masked graph autoencoders
Z. Hou, X. Liu, Y. Cen, Y. Dong, H. Yang, C. Wang, and J. Tang · 2022
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Deep graph learning for anomalous citation detection
J. Liu, F. Xia, X. Feng, J. Ren, and H. Liu · 2022
Universal link predictor by in-context learning
K. Dong, H. Mao, Z. Guo, and N. V. Chawla · 2024
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Universal prompt tuning for graph neural networks
T. Fang, Y. Zhang, Y. Yang, C. Wang, and L. Chen · 2024
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Self-pro: A self-prompt and tuning framework for graph neural networks
C. Gong, X. Li, J. Yu, Y. Cheng, J. Tan, and C. Yu · 2024
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Prodigy: Enabling in-context learning over graphs
Q. Huang, H. Ren, P. Chen, G. Kržmanc, D. Zeng, P. S. Liang, and J. Leskovec · 2024
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One model for one graph: A new perspective for pretraining with cross-domain graphs
J. Liu, H. Mao, Z. Chen, W. Fan, M. Ju, T. Zhao, N. Shah, and J. Tang · 2024
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Emergent abilities of large language models
J. Wei, Y. Tay, R. Bommasani, C. Raffel, B. Zoph, S. Borgeaud, D. Yogatama, M. Bosma, D. Zhou, D. Metzler, et al · 2022
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Simgrace: A simple framework for graph contrastive learning without data augmentation
J. Xia, L. Wu, J. Chen, B. Hu, and S. Z. Li · 2022
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J. Achiam, S. Adler, S. Agarwal, L. Ahmad, I. Akkaya, F. L. Aleman, D. Almeida, J. Altenschmidt, S. Altman, S. Anadkat, et al · 2023
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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 · 2023
Cited alongside, same era.
Towards graph foundation models: A survey and beyond
J. Liu, C. Yang, Z. Lu, J. Chen, Y. Li, M. Zhang, T. Bai, Y. Fang, L. Sun, P. S. Yu, et al · 2023
Cited alongside, same era.
Graphprompt: Unifying pre-training and downstream tasks for graph neural networks
Z. Liu, X. Yu, Y. Fang, and X. Zhang · 2023
Cited alongside, same era.
All in one: Multi-task prompting for graph neural networks
X. Sun, H. Cheng, J. Li, B. Liu, and J. Guan · 2023
Cited alongside, same era.
Y. Tan, H. Lv, X. Huang, J. Zhang, S. Wang, and C. Yang · 2024
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Llms as zero-shot graph learners: Alignment of gnn representations with llm token embeddings
D. Wang, Y. Zuo, F. Li, and J. Wu · 2024
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Gft: Graph foundation model with transferable tree vocabulary
Z. Wang, Z. Zhang, N. Chawla, C. Zhang, and Y. Ye · 2024
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Anygraph: Graph foundation model in the wild
L. Xia 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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Text-free multi-domain graph pre-training: Toward graph foundation models
X. Yu, C. Zhou, Y. Fang, and X. 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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Graphany: A foundation model for node classification on any graph
J. Zhao, H. Mostafa, M. Galkin, M. Bronstein, Z. Zhu, and J. Tang · 2024
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Relief: Reinforcement learning empowered graph feature prompt tuning
J. Zhu, Z. Ding, J. Yu, J. Tan, X. Li, and W. Qian · 2024
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Graphclip: Enhancing transferability in graph foundation models for text-attributed graphs
Y. Zhu, H. Shi, X. Wang, Y. Liu, Y. Wang, B. Peng, C. Hong, and S. Tang · 2024
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