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Graph transformers have emerged as a promising architecture for a variety of graph learning and representation tasks.
Ramanujan graphs
Lubotzky, A., Phillips, R., and Sarnak, P · 1988
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Explicit group-theoretic constructions of combinatorial schemes and their applications in the construction of expanders and concentrators
Margulis, G. A · 1988
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Benchmarking graph neural networks
Dwivedi, V. P., Joshi, C. K., Laurent, T., Bengio, Y., and Bresson, X · 2003
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A proof of Alon’s second eigenvalue conjecture
Friedman, J · 2003
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Expander graphs and their applications
Hoory, S., Linial, N., and Wigderson, A · 2006
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Query-driven active surveying for collective classification
Namata, G. M., London, B., Getoor, L., and Huang, B · 2012
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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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Bresson, X. and Laurent, T · 2017
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Inductive representation learning on large graphs
Hamilton, W. L., Ying, R., 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
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I · 2017
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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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Pitfalls of graph neural network evaluation
Shchur, O., Mumme, M., Bojchevski, A., and Günnemann, S · 2018
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Graph attention networks
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, 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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Relational pooling for graph representations
Murphy, R., Srinivasan, B., Rao, V., and Ribeiro, B · 2019
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Spectral and algebraic graph theory, 2019
Spielman, D. A · 2019
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Improving graph neural network expressivity via subgraph isomorphism counting
Bouritsas, G., Frasca, F., Zafeiriou, S., and Bronstein, M. M · 2020
Cited alongside, same era.
Principal neighbourhood aggregation for graph nets
Corso, G., Cavalleri, L., Beaini, D., Liò, P., and Veličković, P · 2020
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A generalization of transformer networks to graphs
Dwivedi, V. P. and Bresson, X · 2020
Cited alongside, same era.
Hierarchical inter-message passing for learning on molecular graphs
Fey, M., Yuen, J.-G., and Weichert, F · 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.
OGB-LSC: A large-scale challenge for machine learning on graphs
Hu, W., Fey, M., Ren, H., Nakata, M., Dong, Y., and Leskovec, J · 2021
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Edge-augmented graph transformers: Global self-attention is enough for graphs
Hussain, M. S., Zaki, M. J., and Subramanian, D · 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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Random features strengthen graph neural networks
Sato, R., Yamada, M., and Kashima, H · 2021
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Graph neural networks exponentially lose expressive power for node classification
Oono, K. and Suzuki, T · 2020
Cited alongside, same era.
Efficient transformers: A survey
Tay, Y., Dehghani, M., Bahri, D., and Metzler, D · 2020
Cited alongside, same era.
Big Bird: Transformers for longer sequences
Zaheer, M., Guruganesh, G., Dubey, K. A., Ainslie, J., Alberti, C., Ontañón, S., Pham, P., Ravula, A., Wang, Q., Yang, L., and Ahmed, A · 2020
Cited alongside, same era.
Graph-Bert: Only attention is needed for learning graph representations
Zhang, J., Zhang, H., Xia, C., and Sun, L · 2020
Cited alongside, same era.
From stars to subgraphs: Uplifting any GNN with local structure awareness
Zhao, L., Jin, W., Akoglu, L., and Shah, N · 2020
Cited alongside, same era.
Explicit expanders of every degree and size
Alon, N · 2021
Cited alongside, same era.
On the bottleneck of graph neural networks and its practical implications
Alon, U. and Yahav, E · 2021
Cited alongside, same era.
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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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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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Gophormer: Ego-graph transformer for node classification
Zhao, J., Li, C., Wen, Q., Wang, Y., Liu, Y., Sun, H., Xie, X., and Ye, Y · 2021
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Deac, A., Lackenby, M., and Veličković, P · 2022
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Dwivedi, V. P., Rampásek, L., Galkin, M., Parviz, A., Wolf, G., Luu, A. T., and Beaini, D · 2022
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Inferring from references with differences for semi-supervised node classification on graphs
Luo, Y., Luo, G., Yan, K., and Chen, A · 2022
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Recipe for a general, powerful, scalable graph transformer
Rampásek, 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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Nodeformer: A scalable graph structure learning transformer for node classification
Wu, Q., Zhao, W., Li, Z., Wipf, D. P., and Yan, J · 2022
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https://ogb.stanford.edu/docs/leader_nodeprop/#ogbn-arxiv
OGB leaderboard for arxiv dataset · 2023
Closest in time.
Specformer: Spectral graph neural networks meet transformers
Bo, D., Shi, C., Wang, L., and Liao, R · 2023
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Difformer: Scalable (graph) transformers induced by energy constrained diffusion
Wu, Q., Yang, C., Zhao, W., He, Y., Wipf, D., and Yan, J · 2023
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