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Graph Transformer (GT) recently has emerged as a new paradigm of graph learning algorithms, outperforming the previously popular Message Passing Neural Network (MPNN) on multiple benchmarks.
Approximation by superpositions of a sigmoidal function
Cybenko, G · 1989
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Transformers are rnns: Fast autoregressive transformers with linear attention
Katharopoulos, A., Vyas, A., Pappas, N., and Fleuret, F · 2006
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Daily high-resolution blended analyses for sea surface temperature
Reynolds, R. W., Smith, T. M., Liu, C., Chelton, D. B., Casey, K. S., and Schlax, M. G · 2007
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Adam: A method for stochastic optimization
Kingma, D. and Ba, J · 2014
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Neural message passing for quantum chemistry
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., and Dahl, G. E · 2017
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Pointnet: Deep learning on point sets for 3d classification and segmentation
Qi, C. R., Su, H., Mo, K., and Guibas, L. J · 2017
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A simple neural network module for relational reasoning
Santoro, A., Raposo, D., Barrett, D. G., Malinowski, M., Pascanu, R., Battaglia, P., and Lillicrap, T · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
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Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., and Bengio, Y · 2017
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Deep sets
Zaheer, M., Kottur, S., Ravanbakhsh, S., Poczos, B., Salakhutdinov, R. R., and Smola, A. J · 2017
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Relational inductive biases, deep learning, and graph networks
Battaglia, P. W., Hamrick, J. B., Bapst, V., Sanchez-Gonzalez, A., Zambaldi, V., Malinowski, M., Tacchetti, A., Raposo, D., Santoro, A., Faulkner, R., et al · 2018
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Deep learning for physical processes: Incorporating prior scientific knowledge
de Bezenac, E., Pajot, A., and Gallinari, P · 2018
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Deeper insights into graph convolutional networks for semi-supervised learning
Li, Q., Han, Z., and Wu, X.-M · 2018
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Invariant and equivariant graph networks
Maron, H., Ben-Hamu, H., Shamir, N., and Lipman, Y · 2018
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Janossy pooling: Learning deep permutation-invariant functions for variable-size inputs
Murphy, R. L., Srinivasan, B., Rao, V., and Ribeiro, B · 2018
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Generating long sequences with sparse transformers
Child, R., Gray, S., Radford, A., and Sutskever, I · 2019
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Deep learning for physical processes: incorporating prior scientific knowledge
de Bézenac, E., Pajot, A., and Gallinari, P · 2019
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Graph neural networks exponentially lose expressive power for node classification
Oono, K. and Suzuki, T · 2019
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On universal equivariant set networks
Segol, N. and Lipman, Y · 2019
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Pairnorm: Tackling oversmoothing in gnns
Zhao, L. and Akoglu, L · 2019
Cited alongside, same era.
On the bottleneck of graph neural networks and its practical implications
Alon, U. and Yahav, E · 2020
Cited alongside, same era.
A note on over-smoothing for graph neural networks
Cai, C. and Wang, Y · 2020
Cited alongside, same era.
Rethinking attention with performers
Choromanski, K., Likhosherstov, V., Dohan, D., Song, X., Gane, A., Sarlos, T., Hawkins, P., Davis, J., Mohiuddin, A., Kaiser, L., et al · 2020
Cited alongside, same era.
An image is worth 16x16 words: Transformers for image recognition at scale
Representing long-range context for graph neural networks with global attention
Wu, Z., Jain, P., Wright, M., Mirhoseini, A., Gonzalez, J. E., and Stoica, I · 2021
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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
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Structure-aware transformer for graph representation learning
Chen, D., O’Bray, L., and Borgwardt, K · 2022
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Dwivedi, V. P., Rampášek, L., Galkin, M., Parviz, A., Wolf, G., Luu, A. T., and Beaini, D · 2022
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A survey on vision transformer
Han, K., Wang, Y., Chen, H., Chen, X., Guo, J., Liu, Z., Tang, Y., Xiao, A., Xu, C., Xu, Y., et al · 2022
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Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al · 2020
Cited alongside, same era.
A generalization of transformer networks to graphs
Dwivedi, V. P. and Bresson, X · 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.
Efficient transformers: A survey
Tay, Y., Dehghani, M., Bahri, D., and Metzler, D · 2020
Cited alongside, same era.
Transformers: State-of-the-art natural language processing
Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., et al · 2020
Cited alongside, same era.
Revisiting over-smoothing in deep gcns
Yang, C., Wang, R., Yao, S., Liu, S., and Abdelzaher, T · 2020
Cited alongside, same era.
How attentive are graph attention networks?
Brody, S., Alon, U., and Yahav, E · 2021
Cited alongside, same era.
Convit: Improving vision transformers with soft convolutional inductive biases
d’Ascoli, S., Touvron, H., Leavitt, M. L., Morcos, A. S., Biroli, G., and Sagun, L · 2021
Cited alongside, same era.
Hussain, M. S., Zaki, M. J., and Subramanian, D · 2022
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An analysis of virtual nodes in graph neural networks for link prediction
Hwang, E., Thost, V., Dasgupta, S. S., and Ma, T · 2022
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Pure transformers are powerful graph learners
Kim, J., Nguyen, T. D., Min, S., Cho, S., Lee, M., Lee, H., and Hong, S · 2022
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Sign and basis invariant networks for spectral graph representation learning
Lim, D., Robinson, J., Zhao, L., Smidt, T., Sra, S., Maron, H., and Jegelka, S · 2022
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Grpe: Relative positional encoding for graph transformer
Park, W., Chang, W.-G., Lee, D., Kim, J., et al · 2022
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Recipe for a general, powerful, scalable graph transformer
Rampášek, L., Galkin, M., Dwivedi, V. P., Luu, A. T., Wolf, G., and Beaini, D · 2022
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Benchmarking graphormer on large-scale molecular modeling datasets
Shi, Y., Zheng, S., Ke, G., Shen, Y., You, J., He, J., Luo, S., Liu, C., He, D., and Liu, T.-Y · 2022
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Universal approximation of functions on sets
Wagstaff, E., Fuchs, F. B., Engelcke, M., Osborne, M. A., and Posner, I · 2022
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Meta-learning dynamics forecasting using task inference
Wang, R., Walters, R., and Yu, R · 2022
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Nodeformer: A scalable graph structure learning transformer for node classification
Wu, Q., Zhao, W., Li, Z., Wipf, D., and Yan, J · 2022
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Exponential separations in symmetric neural networks
Zweig, A. and Bruna, J · 2022
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Transformers meet directed graphs
Geisler, S., Li, Y., Mankowitz, D., Cemgil, A. T., Günnemann, S., and Paduraru, C · 2023
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Attending to graph transformers
Müller, L., Galkin, M., Morris, C., and Rampášek, L · 2023
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