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Graph Transformer, due to its global attention mechanism, has emerged as a new tool in dealing with graph-structured data.
A fast and high quality multilevel scheme for partitioning irregular graphs
Karypis, G. and Kumar, V · 1998
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Birds of a feather: Homophily in social networks
McPherson, M., Smith-Lovin, L., and Cook, J. M · 2001
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The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains
Shuman, D. I., Narang, S. K., Frossard, P., Ortega, A., and Vandergheynst, P · 2013
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Distilling the knowledge in a neural network
Hinton, G., Vinyals, O., and Dean, J · 2015
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Ba, J. L., Kiros, J. R., and Hinton, G. E · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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How to learn a graph from smooth signals
Kalofolias, V · 2016
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Revisiting semi-supervised learning with graph embeddings
Yang, Z., Cohen, W., and Salakhudinov, R · 2016
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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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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
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A neural network approach to jointly modeling social networks and mobile trajectories
Yang, C., Sun, M., Zhao, W. X., Liu, Z., and Chang, E. Y · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 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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Graph attention networks
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., and Bengio, Y · 2018
Cited alongside, same era.
Fast graph representation learning with pytorch geometric
Fey, M. and Lenssen, J. E · 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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Rethinking attention with performers
Choromanski, K., Likhosherstov, V., Dohan, D., Song, X., Gane, A., Sarlos, T., Hawkins, P., Davis, J., Mohiuddin, A., Kaiser, L., et al · 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.
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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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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Nagphormer: A tokenized graph transformer for node classification in large graphs
Chen, J., Gao, K., Li, G., and He, K · 2022
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Expander graph propagation
Deac, A., Lackenby, M., and Veličković, P · 2022
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Graph neural networks exponentially lose expressive power for node classification
Oono, K. and Suzuki, T · 2020
Cited alongside, same era.
Geom-gcn: Geometric graph convolutional networks
Pei, H., Wei, B., Chang, K. C.-C., Lei, Y., and Yang, B · 2020
Cited alongside, same era.
Linformer: Self-attention with linear complexity
Wang, S., Li, B. Z., Khabsa, M., Fang, H., and Ma, H · 2020
Cited alongside, same era.
Beyond low-frequency information in graph convolutional networks
Bo, D., Wang, X., Shi, C., and Shen, H · 2021
Cited alongside, same era.
Adaptive universal generalized pagerank graph neural network
Chien, E., Peng, J., Li, P., and Milenkovic, O · 2021
Cited alongside, same era.
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 · 2021
Cited alongside, same era.
Rethinking graph transformers with spectral attention
Kreuzer, D., Beaini, D., Hamilton, W., Létourneau, V., and Tossou, P · 2021
Cited alongside, same era.
Kuang, W., WANG, Z., Li, Y., Wei, Z., and Ding, B · 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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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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Hierarchical graph transformer with adaptive node sampling
Zhang, Z., Liu, Q., Hu, Q., and Lee, C.-K · 2022
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Goat: A global transformer on large-scale graphs
Kong, K., Chen, J., Kirchenbauer, J., Ni, R., Bruss, C. B., and Goldstein, T · 2023
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Simplifying and empowering transformers for large-graph representations
Wu, Q., Zhao, W., Yang, C., Zhang, H., Nie, F., Jiang, H., Bian, Y., and Yan, J · 2023
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Learning to count isomorphisms with graph neural networks
Yu, X., Liu, Z., Fang, Y., and Zhang, X · 2023
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Hierarchical transformer for scalable graph learning
Zhu, W., Wen, T., Song, G., Ma, X., and Wang, L · 2023
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