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We propose a novel positional encoding for learning graph on Transformer architecture.
Laplacian eigenmaps for dimensionality reduction and data representation
Belkin, M.; and Niyogi, P. 2003 · 2003
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Benchmarking graph neural networks
Dwivedi, V. P.; Joshi, C. K.; Laurent, T.; Bengio, Y.; and Bresson, X. 2020 · 2003
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Principal neighbourhood aggregation for graph nets
Corso, G.; Cavalleri, L.; Beaini, D.; Liò, P.; and Veličković, P. 2020 · 2004
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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 · 2005
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Deepergcn: All you need to train deeper gcns
Li, G.; Xiong, C.; Thabet, A.; and Ghanem, B. 2020 · 2006
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Self-supervised graph transformer on large-scale molecular data
Rong, Y.; Bian, Y.; Xu, T.; Xie, W.; Wei, Y.; Huang, W.; and Huang, J. 2020 · 2007
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Graph convolutions that can finally model local structure
Brossard, R.; Frigo, O.; and Dehaene, D. 2020 · 2011
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A generalization of transformer networks to graphs
Dwivedi, V. P.; and Bresson, X. 2020 · 2012
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Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
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Semi-supervised classification with graph convolutional networks
Kipf, T. N.; and Welling, M. 2016 · 2016
Cited alongside, same era.
Bresson, X.; and Laurent, T. 2017 · 2017
Cited alongside, same era.
Neural message passing for quantum chemistry
Gilmer, J.; Schoenholz, S. S.; Riley, P. F.; Vinyals, O.; and Dahl, G. E. 2017 · 2017
Cited alongside, same era.
Attention is all you need
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, Ł.; and Polosukhin, I. 2017 · 2017
Cited alongside, same era.
How powerful are graph neural networks?
Xu, K.; Hu, W.; Leskovec, J.; and Jegelka, S. 2018 · 2018
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Directional graph networks
Beani, D.; Passaro, S.; Létourneau, V.; Hamilton, W.; Corso, G.; and Liò, P. 2021 · 2021
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Graphnorm: A principled approach to accelerating graph neural network training
Cai, T.; Luo, S.; Xu, K.; He, D.; Liu, T.-y.; and Wang, L. 2021 · 2021
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Edge-augmented graph transformers: Global self-attention is enough for graphs
Hussain; Shamim, M.; Zaki; J, M.; and Subramanian, D. 2021 · 2021
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Rethinking Graph Transformers with Spectral Attention
Kreuzer, D.; Beaini, D.; Hamilton, W. L.; Létourneau, V.; and Tossou, P. 2021 · 2021
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Veličković, P.; Cucurull, G.; Casanova, A.; Romero, A.; Lio, P.; and Bengio, Y. 2017 · 2017
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2018 · 2018
Cited alongside, same era.
Self-attention with relative position representations
Shaw, P.; Uszkoreit, J.; and Vaswani, A. 2018 · 2018
Cited alongside, same era.
Parameterized hypercomplex graph neural networks for graph classification
Le, T.; Bertolini, M.; Noé, F.; and Clevert, D.-A. 2021 · 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 · 2021
Later among the works it cites.