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Graph Neural Networks (GNNs) have empowered the advance in graph-structured data analysis.
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Langley, P · 2000
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Convolutional neural networks on graphs with fast localized spectral filtering
Defferrard, M., Bresson, X., and Vandergheynst, P · 2016
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Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 2016
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Revisiting semi-supervised learning with graph embeddings
Yang, Z., Cohen, W., and Salakhudinov, R · 2016
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Inductive representation learning on large graphs
Hamilton, W., Ying, Z., and Leskovec, J · 2017
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Semi-supervised classification with graph convolutional networks
Kipf, T. and Welling, M · 2017
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Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., and Bengio, Y · 2017
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Fastgcn: fast learning with graph convolutional networks via importance sampling
Chen, J., Ma, T., and Xiao, C · 2018
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Large-scale learnable graph convolutional networks
Gao, H., Wang, Z., and Ji, S · 2018
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Attention-based graph neural network for semi-supervised learning
Thekumparampil, K. K., Wang, C., Oh, S., and Li, L.-J · 2018
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Graph attention networks
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., and Bengio, Y · 2018
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How powerful are graph neural networks?
Xu, K., Hu, W., Leskovec, J., and Jegelka, S · 2018
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Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks
Chiang, W.-L., Liu, X., Si, S., Li, Y., Bengio, S., and Hsieh, C.-J · 2019
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Roberta: A robustly optimized bert pretraining approach
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V · 2019
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Sentence-bert: Sentence embeddings using siamese bert-networks
Reimers, N. and Gurevych, I · 2019
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Deep graph infomax
Veličković, P., Fedus, W., Hamilton, W. L., Liò, P., Bengio, Y., and Hjelm, R. D · 2019
Cited alongside, same era.
Simplifying graph convolutional networks
Wu, F., Souza, A., Zhang, T., Fifty, C., Yu, T., and Weinberger, K · 2019
Cited alongside, same era.
Graph transformer networks
Yun, S., Jeong, M., Kim, R., Kang, J., and Kim, H. J · 2019
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A generalization of transformer networks to graphs
Dwivedi, V. P. and Bresson, X · 2020
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Contrastive multi-view representation learning on graphs
Hassani, K. and Khasahmadi, A. H · 2020
Cited alongside, same era.
Open graph benchmark: Datasets for machine learning on graphs
Hu, W., Fey, M., Zitnik, M., Dong, Y., Ren, H., Liu, B., Catasta, M., and Leskovec, J · 2020
Cited alongside, same era.
Nodeformer: A scalable graph structure learning transformer for node classification
Wu, Q., Zhao, W., Li, Z., Wipf, D. P., and Yan, J · 2022
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Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al · 2023
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Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality, March 2023
Chiang, W.-L., Li, Z., Lin, Z., Sheng, Y., Wu, Z., Zhang, H., Zheng, L., Zhuang, S., Zhuang, Y., Gonzalez, J. E., Stoica, I., and Xing, E. P · 2023
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Simteg: A frustratingly simple approach improves textual graph learning
Duan, K., Liu, Q., Chua, T.-S., Yan, S., Ooi, W. T., Xie, Q., and He, J · 2023
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Talk like a graph: Encoding graphs for large language models
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L2-gcn: Layer-wise and learned efficient training of graph convolutional networks
You, Y., Chen, T., Wang, Z., and Shen, Y · 2020
Cited alongside, same era.
Automated self-supervised learning for graphs
Jin, W., Liu, X., Zhao, X., Ma, Y., Shah, N., and Tang, J · 2021
Cited alongside, same era.
Scalable and adaptive graph neural networks with self-label-enhanced training
Sun, C., Gu, H., and Hu, J · 2021
Cited alongside, same era.
Do transformers really perform badly for graph representation?
Ying, C., Cai, T., Luo, S., Zheng, S., Ke, G., He, D., Shen, Y., and Liu, T.-Y · 2021
Cited alongside, same era.
From canonical correlation analysis to self-supervised graph neural networks
Zhang, H., Wu, Q., Yan, J., Wipf, D., and Yu, P. S · 2021
Cited alongside, same era.
Nagphormer: A tokenized graph transformer for node classification in large graphs
Chen, J., Gao, K., Li, G., and He, K · 2022
Cited alongside, same era.
Fatemi, B., Halcrow, J., and Perozzi, B · 2023
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Guo, J., Du, L., and Liu, H · 2023
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He, X., Bresson, X., Laurent, T., Perold, A., LeCun, Y., and Hooi, B · 2023
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Can llms effectively leverage graph structural information: when and why
Huang, J., Zhang, X., Mei, Q., and Ma, J · 2023
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Multi-task self-supervised graph neural networks enable stronger task generalization
Ju, M., Zhao, T., Wen, Q., Yu, W., Shah, N., Ye, Y., and Zhang, C · 2023
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Liu, H., Li, C., Wu, Q., and Lee, Y. J · 2023
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Graphgpt: Graph instruction tuning for large language models
Tang, J., Yang, Y., Wei, W., Shi, L., Su, L., Cheng, S., Yin, D., and Huang, C · 2023
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Llama: Open and efficient foundation language models
Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al · 2023
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Visionllm: Large language model is also an open-ended decoder for vision-centric tasks
Wang, W., Chen, Z., Chen, X., Wu, J., Zhu, X., Zeng, G., Luo, P., Lu, T., Zhou, J., Qiao, Y., et al · 2023
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Natural language is all a graph needs
Ye, R., Zhang, C., Wang, R., Xu, S., and Zhang, Y · 2023
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