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Recently, the emerging graph Transformers have made significant advancements for node classification on graphs.
T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” in Proceedings of the International Conference on Learning Representations , 2017
2017
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J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals, and G. E. Dahl, “Neural message passing for quantum chemistry,” in Proceedings of the International Conference on Machine Learning , vol. 70, 2017, pp. 1263–1272
2017
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A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in Proceedings of the Annual Conference on Neural Information Processing Systems , vol. 30, 2017, pp. 5998–6008
2017
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P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio, “Graph Attention Networks,” in Proceedings of the International Conference on Learning Representations , 2018
2018
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K. Xu, C. Li, Y. Tian, T. Sonobe, K. Kawarabayashi, and S. Jegelka, “Representation learning on graphs with jumping knowledge networks,” in Proceedings of the International Conference on Machine Learning , vol. 80, 2018, pp. 5449–5458
2018
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S. Abu-El-Haija, B. Perozzi, A. Kapoor, N. Alipourfard, K. Lerman, H. Harutyunyan, G. V. Steeg, and A. Galstyan, “Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing,” in Proceedings of the International Conference on Machine Learning , vol. 97, 2019, pp. 21–29
2019
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J. Klicpera, S. Weißenberger, and S. Günnemann, “Diffusion Improves Graph Learning,” in Proceedings of the Annual Conference on Neural Information Processing Systems , 2019, pp. 13 333–13 345
2019
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J. Klicpera, A. Bojchevski, and S. Günnemann, “Predict then Propagate: Graph Neural Networks meet Personalized PageRank,” in Proceedings of the International Conference on Learning Representations , 2019
2019
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F. Wu, A. Souza, T. Zhang, C. Fifty, T. Yu, and K. Weinberger, “Simplifying Graph Convolutional Networks,” in Proceedings of the International Conference on Machine Learning , 2019, pp. 6861–6871
2019
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I. Loshchilov and F. Hutter, “Decoupled weight decay regularization,” in Proceedings of the International Conference on Learning Representations , 2019
2019
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K. Gopinath, C. Desrosiers, and H. Lombaert, “Learnable pooling in graph convolutional networks for brain surface analysis,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 44, no. 2, pp. 864–876, 2020
2020
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M. Chen, Z. Wei, Z. Huang, B. Ding, and Y. Li, “Simple and deep graph convolutional networks,” in Proceedings of the International Conference on Machine Learning , vol. 119, 2020, pp. 1725–1735
2020
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D. Chen, Y. Lin, W. Li, P. Li, J. Zhou, and X. Sun, “Measuring and relieving the over-smoothing problem for graph neural networks from the topological view,” in Proceedings of the AAAI Conference on Artificial Intelligence , 2020
2020
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2020
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J. Zhu, Y. Yan, L. Zhao, M. Heimann, L. Akoglu, and D. Koutra, “Beyond homophily in graph neural networks: Current limitations and effective designs,” in Proceedings of the Annual Conference on Neural Information Processing Systems , 2020
2020
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H. Pei, B. Wei, K. C. Chang, Y. Lei, and B. Yang, “Geom-gcn: Geometric graph convolutional networks,” in Proceedings of the International Conference on Learning Representations , 2020
2020
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X. Wang, M. Zhu, D. Bo, P. Cui, C. Shi, and J. Pei, “AM-GCN: adaptive multi-channel graph convolutional networks,” in Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining , 2020, pp. 1243–1253
2020
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U. Alon and E. Yahav, “On the bottleneck of graph neural networks and its practical implications,” in Proceedings of the International Conference on Learning Representations , 2021
2021
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C. Ying, T. Cai, S. Luo, S. Zheng, G. Ke, D. He, Y. Shen, and T.-Y. Liu, “Do transformers really perform badly for graph representation,” in Proceedings of the Annual Conference on Neural Information Processing Systems , vol. 34, 2021, pp. 28 877–28 888
2021
Cited alongside, same era.
D. Kreuzer, D. Beaini, W. Hamilton, V. Létourneau, and P. Tossou, “Rethinking Graph Transformers with Spectral Attention,” in Proceedings of the Annual Conference on Neural Information Processing Systems , vol. 34, 2021, pp. 21 618–21 629
2021
Cited alongside, same era.
2021
Cited alongside, same era.
W. Jin, T. Derr, Y. Wang, Y. Ma, Z. Liu, and J. Tang, “Node similarity preserving graph convolutional networks,” in Proceedings of the ACM International Conference on Web Search and Data Mining , 2021, pp. 148–156
2021
Q. Wu, W. Zhao, Z. Li, D. Wipf, and J. Yan, “Nodeformer: A scalable graph structure learning transformer for node classification,” in Proceedings of the Annual Conference on Neural Information Processing Systems , vol. 35, 2022, pp. 27 387–27 401
2022
Later among the works it cites.
J. Kim, D. Nguyen, S. Min, S. Cho, M. Lee, H. Lee, and S. Hong, “Pure transformers are powerful graph learners,” in Proceedings of the Annual Conference on Neural Information Processing Systems , 2022
2022
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X. Li, R. Zhu, Y. Cheng, C. Shan, S. Luo, D. Li, and W. Qian, “Finding global homophily in graph neural networks when meeting heterophily,” in Proceedings of the International Conference on Machine Learning , vol. 162, 2022, pp. 13 242–13 256
2022
Later among the works it cites.
X. Ma, Q. Chen, Y. Wu, G. Song, L. Wang, and B. Zheng, “Rethinking structural encodings: Adaptive graph transformer for node classification task,” in Proceedings of the ACM Web Conference , 2023, pp. 533–544
2023
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Cited alongside, same era.
D. Kim and A. Oh, “How to find your friendly neighborhood: Graph attention design with self-supervision,” in Proceedings of the International Conference on Learning Representations , 2021
2021
Cited alongside, same era.
D. Bo, X. Wang, C. Shi, and H. Shen, “Beyond low-frequency information in graph convolutional networks,” in Proceedings of the AAAI Conference on Artificial Intelligence , 2021, pp. 3950–3957
2021
Cited alongside, same era.
H. Dong, J. Chen, F. Feng, X. He, S. Bi, Z. Ding, and P. Cui, “On the equivalence of decoupled graph convolution network and label propagation,” in Proceedings of the Web Conference , 2021, pp. 3651–3662
2021
Cited alongside, same era.
E. Chien, J. Peng, P. Li, and O. Milenkovic, “Adaptive Universal Generalized PageRank Graph Neural Network,” in Proceedings of the International Conference on Learning Representations , 2021
2021
Cited alongside, same era.
P. Jain, Z. Wu, M. Wright, A. Mirhoseini, J. E. Gonzalez, and I. Stoica, “Representing Long-Range Context for Graph Neural Networks with Global Attention,” in Proceedings of the Annual Conference on Neural Information Processing Systems , 2021, pp. 13 266–13 279
2021
Cited alongside, same era.
W. Jin, T. Derr, Y. Wang, Y. Ma, Z. Liu, and J. Tang, “Node similarity preserving graph convolutional networks,” in Proceedings of the ACM International Conference on Web Search and Data Mining , 2021, pp. 148–156
2021
Cited alongside, same era.
L. Rampásek, M. Galkin, V. P. Dwivedi, A. T. Luu, G. Wolf, and D. Beaini, “Recipe for a General, Powerful, Scalable Graph Transformer,” in Proceedings of the Annual Conference on Neural Information Processing Systems , vol. 35, 2022, pp. 14 501–14 515
2022
Cited alongside, same era.
2022
Cited alongside, same era.
Later among the works it cites.
2023
Later among the works it cites.
J. Chen, K. Gao, G. Li, and K. He, “Nagphormer: A tokenized graph transformer for node classification in large graphs,” in Proceedings of the International Conference on Learning Representations , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
D. Bo, C. Shi, L. Wang, and R. Liao, “Specformer: Spectral graph neural networks meet transformers,” in Proceedings of the International Conference on Learning Representations , 2023
2023
Later among the works it cites.
Q. Wu, C. Yang, W. Zhao, Y. He, D. Wipf, and J. Yan, “Difformer: Scalable (graph) transformers induced by energy constrained diffusion,” in Proceedings of the Eleventh International Conference on Learning Representations , 2023
2023
Later among the works it cites.
S. Huang, Y. Song, J. Zhou, and Z. Lin, “Tailoring self-attention for graph via rooted subtrees,” in Proceedings of the Annual Conference on Neural Information Processing Systems , 2023
2023
Later among the works it cites.
C. Liu, Y. Zhan, X. Ma, L. Ding, D. Tao, J. Wu, and W. Hu, “Gapformer: Graph transformer with graph pooling for node classification,” in Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence , 2023, pp. 2196–2205
2023
Later among the works it cites.
O. Platonov, D. Kuznedelev, M. Diskin, A. Babenko, and L. Prokhorenkova, “A critical look at the evaluation of gnns under heterophily: Are we really making progress?” in Proceedings of the Eleventh International Conference on Learning Representations , 2023
2023
Later among the works it cites.
Q. Wu, W. Zhao, C. Yang, H. Zhang, F. Nie, H. Jiang, Y. Bian, and J. Yan, “Simplifying and empowering transformers for large-graph representations,” in Proceedings of the Annual Conference on Neural Information Processing Systems , 2023
2023
Later among the works it cites.
S. Huang, Y. Song, J. Zhou, and Z. Lin, “Tailoring self-attention for graph via rooted subtrees,” in Proceedings of the Annual Conference on Neural Information Processing Systems , 2023
2023
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2024
Closest in time.
J. Chen, B. Li, Q. He, and K. He, “Pamt: A novel propagation-based approach via adaptive similarity mask for node classification,” IEEE Transactions on Computational Social Systems , 2024
2024
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J. Chen, B. Li, and K. He, “Neighborhood convolutional graph neural network,” Knowledge-Based Systems , vol. 295, p. 111861, 2024
2024
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