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Transformers for graph data are increasingly widely studied and successful in numerous learning tasks.
Multilayer feedforward networks are universal approximators
Hornik, K., Stinchcombe, M., and White, H · 1989
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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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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S. and Szegedy, C · 2015
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Layer Normalization
Ba, J. L., Kiros, J. R., and Hinton, G. E · 2016
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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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Semi-Supervised Classification with Graph Convolutional Networks
Kipf, T. N. and Welling, M · 2017
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Attention is All you Need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Aidan N Gomez, Kaiser, L., and Polosukhin, I · 2017
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Residual Gated Graph ConvNets
Bresson, X. and Laurent, T · 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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Film: Visual reasoning with a general conditioning layer
Perez, E., Strub, F., De Vries, H., Dumoulin, V., and Courville, A · 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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Transformer-XL: Attentive Language Models beyond a Fixed-Length Context
Dai, Z., Yang, Z., Yang, Y., Carbonell, J., Le, Q., and Salakhutdinov, R · 2019
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2019
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Diffusion Improves Graph Learning
Gasteiger, J., Weiß enberger, S., and Günnemann, S · 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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How Powerful are Graph Neural Networks?
Xu, K., Hu, W., Leskovec, J., and Jegelka, S · 2019
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Position-aware Graph Neural Networks
You, J., Ying, R., and Leskovec, J · 2019
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On the Bottleneck of Graph Neural Networks and its Practical Implications
Alon, U. and Yahav, E · 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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An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., and Houlsby, N · 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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Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation Learning
Li, P., Wang, Y., Wang, H., and Leskovec, J · 2020
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What graph neural networks cannot learn: Depth vs width
Loukas, A · 2020
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Weisfeiler and leman go sparse: towards scalable higher-order graph embeddings
Morris, C., Rattan, G., and Mutzel, P · 2020
Cited alongside, same era.
Graph neural networks exponentially lose expressive power for node classification
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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Identity-aware Graph Neural Networks
You, J., Gomes-Selman, J., Ying, R., and Leskovec, J · 2021
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Eigen-GNN: A Graph Structure Preserving Plug-in for GNNs
Zhang, Z., Cui, P., Pei, J., Wang, X., and Zhu, W · 2021
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Equivariant Subgraph Aggregation Networks
Bevilacqua, B., Frasca, F., Lim, D., Srinivasan, B., Cai, C., Balamurugan, G., Bronstein, M. M., and Maron, H · 2022
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Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting
Bouritsas, G., Frasca, F., Zafeiriou, S. P., and Bronstein, M · 2022
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How Attentive are Graph Attention Networks?
Brody, S., Alon, U., and Yahav, E · 2022
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Oono, K. and Suzuki, T · 2020
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On the Equivalence between Positional Node Embeddings and Structural Graph Representations
Srinivasan, B. and Ribeiro, B · 2020
Cited alongside, same era.
Directional Graph Networks
Beani, D., Passaro, S., Létourneau, V., Hamilton, W., Corso, G., and Lió, P · 2021
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Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks
Bodnar, C., Frasca, F., Wang, Y., Otter, N., Montufar, G. F., Lió, P., and Bronstein, M · 2021
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Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
Bronstein, M. M., Bruna, J., Cohen, T., and Veličković, P · 2021
Cited alongside, same era.
A Generalization of Transformer Networks to Graphs
Dwivedi, V. P. and Bresson, X · 2021
Cited alongside, same era.
Graph Neural Networks with Learnable Structural and Positional Representations
Dwivedi, V. P., Luu, A. T., Laurent, T., Bengio, Y., and Bresson, X · 2021
Cited alongside, same era.
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Structure-Aware Transformer for Graph Representation Learning
Chen, D., O’Bray, L., and Borgwardt, K · 2022
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Relational attention: Generalizing transformers for graph-structured tasks
Diao, C. and Loynd, R · 2022
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Weisfeiler and leman go infinite: Spectral and combinatorial pre-colorings
Feldman, O., Boyarski, A., Feldman, S., Kogan, D., Mendelson, A., and Baskin, C · 2022
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Global Self-Attention as a Replacement for Graph Convolution
Hussain, M. S., Zaki, M. J., and Subramanian, D · 2022
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Pure Transformers are Powerful Graph Learners
Kim, J., Nguyen, D. T., Min, S., Cho, S., Lee, M., Lee, H., and Hong, S · 2022
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Your transformer may not be as powerful as you expect
Luo, S., Li, S., Zheng, S., Liu, T.-Y., Wang, L., and He, D · 2022
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GPS++: An Optimised Hybrid MPNN/Transformer for Molecular Property Prediction, December 2022
Masters, D., Dean, J., Klaser, K., Li, Z., Maddrell-Mander, S., Sanders, A., Helal, H., Beker, D., Rampášek, L., and Beaini, D · 2022
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GRPE: Relative Positional Encoding for Graph Transformer
Park, W., Chang, W., Lee, D., Kim, J., and Hwang, S.-w · 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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Understanding over-squashing and bottlenecks on graphs via curvature
Topping, J., Giovanni, F. D., Chamberlain, B. P., Dong, X., and Bronstein, M. M · 2022
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Equivariant and Stable Positional Encoding for More Powerful Graph Neural Networks
Wang, H., Yin, H., Zhang, M., and Li, P · 2022
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Sign and Basis Invariant Networks for Spectral Graph Representation Learning
Lim, D., Robinson, J. D., Zhao, L., Smidt, T., Sra, S., Maron, H., and Jegelka, S · 2023
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Rethinking the expressive power of GNNs via graph biconnectivity
Zhang, B., Luo, S., Wang, L., and He, D · 2023
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