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The Transformer architecture has recently gained considerable attention in the field of graph representation learning, as it naturally overcomes several limitations of Graph Neural Networks (GNNs) with customized attention mechanisms or positional and structural encodings.
The pascal visual object classes (voc) challenge
Everingham, M., Van Gool, L., Williams, C. K., Winn, J., and Zisserman, A · 2010
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Zinc: a free tool to discover chemistry for biology
Irwin, J. J., Sterling, T., Mysinger, M. M., Bolstad, E. S., and Coleman, R. G · 2012
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Translating embeddings for modeling multi-relational data
Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., and Yakhnenko, O · 2013
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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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Satpdb: a database of structurally annotated therapeutic peptides
Singh, S., Chaudhary, K., Dhanda, S. K., Bhalla, S., Usmani, S. S., Gautam, A., Tuknait, A., Agrawal, P., Mathur, D., and Raghava, G. P · 2016
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Bresson, X. and Laurent, T · 2017
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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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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. N. and Welling, M · 2017
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SGDR: Stochastic gradient descent with warm restarts
Loshchilov, I. and Hutter, F · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L. u., and Polosukhin, I · 2017
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Community detection and stochastic block models: recent developments
Abbe, E · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
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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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Network embedding as matrix factorization: Unifying deepwalk, line, pte, and node2vec
Qiu, J., Dong, Y., Ma, H., Li, J., Wang, K., and Tang, 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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Generative models for graph-based protein design
Ingraham, J., Garg, V., Barzilay, R., and Jaakkola, T · 2019
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Decoupled weight decay regularization
Loshchilov, I. and Hutter, F · 2019
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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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Relational pooling for graph representations
Murphy, R., Srinivasan, B., Rao, V., and Ribeiro, B · 2019
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How powerful are graph neural networks?
Xu, K., Hu, W., Leskovec, J., and Jegelka, S · 2019
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Measuring and relieving the over-smoothing problem for graph neural networks from the topological view
Chen, D., Lin, Y., Li, W., Li, P., Zhou, J., and Sun, X · 2020
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Principal neighbourhood aggregation for graph nets
Corso, G., Cavalleri, L., Beaini, D., Liò, P., and Veličković, P · 2020
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Rethinking graph transformers with spectral attention
Kreuzer, D., Beaini, D., Hamilton, W., Létourneau, V., and Tossou, P · 2021
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Random features strengthen graph neural networks
Sato, R., Yamada, M., and Kashima, H · 2021
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Graph learning with 1d convolutions on random walks
Toenshoff, J., Ritzert, M., Wolf, H., and Grohe, M · 2021
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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
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A generalization of transformer networks to graphs
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Benchmarking graph neural networks
Dwivedi, V. P., Joshi, C. K., Luu, A. T., Laurent, T., Bengio, Y., and Bresson, X · 2020
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Strategies for pre-training graph neural networks
Hu, W., Liu, B., Gomes, J., Zitnik, M., Liang, P., Pande, V., and Leskovec, J · 2020
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Graph neural networks exponentially lose expressive power for node classification
Oono, K. and Suzuki, T · 2020
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Pinnersage: Multi-modal user embedding framework for recommendations at pinterest
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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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Chen, D., O’Bray, L., and Borgwardt, K · 2022
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Beyond self-attention: External attention using two linear layers for visual tasks
Guo, M.-H., Liu, Z.-N., Mu, T.-J., and Hu, S.-M · 2022
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Global self-attention as a replacement for graph convolution
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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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From stars to subgraphs: Uplifting any GNN with local structure awareness
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Improving graph neural network expressivity via subgraph isomorphism counting
Bouritsas, G., Frasca, F., Zafeiriou, S., and Bronstein, M. M · 2023
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Drew: Dynamically rewired message passing with delay
Gutteridge, B., Dong, X., Bronstein, M. M., and Di Giovanni, F · 2023
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A generalization of ViT/MLP-mixer to graphs
He, X., Hooi, B., Laurent, T., Perold, A., Lecun, Y., and Bresson, X · 2023
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Graph inductive biases in transformers without message passing
Ma, L., Lin, C., Lim, D., Romero-Soriano, A., Dokania, P. K., Coates, M., Torr, P., and Lim, S.-N · 2023
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Exphormer: Sparse transformers for graphs
Shirzad, H., Velingker, A., Venkatachalam, B., Sutherland, D. J., and Sinop, A. K · 2023
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Lgi-gt: graph transformers with local and global operators interleaving
Yin, S. and Zhong, G · 2023
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