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The recent works proposing transformer-based models for graphs have proven the inadequacy of Vanilla Transformer for graph representation learning.
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Hamilton, W., Ying, Z., and Leskovec, J · 2017
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Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 2017
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Attention is all you need
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Graph signal processing: Overview, challenges, and applications
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Tudataset: A collection of benchmark datasets for learning with graphs
Morris, C., Kriege, N. M., Bause, F., Kersting, K., Mutzel, P., and Neumann, M · 2020
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Masked label prediction: Unified message passing model for semi-supervised classification
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O (n) connections are expressive enough: Universal approximability of sparse transformers
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Graph-bert: Only attention is needed for learning graph representations
Zhang, J., Zhang, H., Xia, C., and Sun, L · 2020
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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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Weisfeiler and leman go neural: Higher-order graph neural networks
Morris, C., Ritzert, M., Fey, M., Hamilton, W. L., Lenssen, J. E., Rattan, G., and Grohe, M · 2019
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Revisiting graph neural networks: All we have is low-pass filters
Nt, H. and Maehara, T · 2019
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Simplifying graph convolutional networks
Wu, F., Souza, A., Zhang, T., Fifty, C., Yu, T., and Weinberger, K · 2019
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Pairnorm: Tackling oversmoothing in gnns
Zhao, L. and Akoglu, L · 2020
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Graph neural networks: A review of methods and applications
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On the bottleneck of graph neural networks and its practical implications
Alon, U. and Yahav, E · 2021
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Directional graph networks
Beaini, D., Passaro, S., Létourneau, V., Hamilton, W. L., Corso, G., and Liò, P · 2021
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Graph neural networks with convolutional arma filters
Bianchi, F. M., Grattarola, D., Livi, L., and Alippi, C · 2021
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Graph kernel attention transformers, 2021
Choromanski, K., Lin, H., Chen, H., and Parker-Holder, J · 2021
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Message passing in graph convolution networks via adaptive filter banks
Gao, X., Dai, W., Li, C., Zou, J., Xiong, H., and Frossard, P · 2021
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Bernnet: Learning arbitrary graph spectral filters via bernstein approximation
He, M., Wei, Z., Huang, Z., and Xu, H · 2021
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Rethinking graph transformers with spectral attention
Kreuzer, D., Beaini, D., Hamilton, W. L., Létourneau, V., and Tossou, P · 2021
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Graphit: Encoding graph structure in transformers
Mialon, G., Chen, D., Selosse, M., and Mairal, J · 2021
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Do transformers really perform bad 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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Graph neural networks with learnable structural and positional representations
Dwivedi, V. P., Luu, A. T., Laurent, T., Bengio, Y., and Bresson, X · 2022
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How do vision transformers work?
Park, N. and Kim, S · 2022
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