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Recently, Graph Transformers have emerged as a promising solution to alleviate the inherent limitations of Graph Neural Networks (GNNs) and enhance graph representation performance.
W. Maass, “Networks of spiking neurons: the third generation of neural network models,” Neural networks , vol. 10, no. 9, pp. 1659–1671, 1997
1997
Earlier work this paper cites.
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg et al. , “Scikit-learn: Machine learning in python,” the Journal of machine Learning research , vol. 12, pp. 2825–2830, 2011
2011
Earlier work this paper cites.
T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” in International Conference on Learning Representations , 2016
2016
Earlier work this paper cites.
O. Vinyals, C. Blundell, T. Lillicrap, D. Wierstra et al. , “Matching networks for one shot learning,” Advances in neural information processing systems , vol. 29, 2016
2016
Earlier work this paper cites.
P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio, “Graph attention networks,” in International Conference on Learning Representations , 2018
2018
Earlier work this paper cites.
F. Wu, A. Souza, T. Zhang, C. Fifty, T. Yu, and K. Weinberger, “Simplifying graph convolutional networks,” in International conference on machine learning . PMLR, 2019, pp. 6861–6871
2019
Earlier work this paper cites.
J. Zhu, Y. Yan, L. Zhao, M. Heimann, L. Akoglu, and D. Koutra, “Beyond homophily in graph neural networks: Current limitations and effective designs,” Advances in neural information processing systems , vol. 33, pp. 7793–7804, 2020
2020
Earlier work this paper cites.
Z. Yang, M. Ding, C. Zhou, H. Yang, J. Zhou, and J. Tang, “Understanding negative sampling in graph representation learning,” in Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining , 2020, pp. 1666–1676
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
W. Hu, M. Fey, M. Zitnik, Y. Dong, H. Ren, B. Liu, M. Catasta, and J. Leskovec, “Open graph benchmark: Datasets for machine learning on graphs,” Advances in neural information processing systems , vol. 33, pp. 22 118–22 133, 2020
2020
Earlier work this paper cites.
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?” Advances in neural information processing systems , vol. 34, pp. 28 877–28 888, 2021
2021
Earlier work this paper cites.
Z. Wu, P. Jain, M. Wright, A. Mirhoseini, J. E. Gonzalez, and I. Stoica, “Representing long-range context for graph neural networks with global attention,” Advances in Neural Information Processing Systems , vol. 34, pp. 13 266–13 279, 2021
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
2021
Cited alongside, same era.
Q. Wu, W. Zhao, Z. Li, D. P. Wipf, and J. Yan, “Nodeformer: A scalable graph structure learning transformer for node classification,” Advances in Neural Information Processing Systems , vol. 35, pp. 27 387–27 401, 2022
2022
Cited alongside, same era.
Z. Zhang, Q. Liu, Q. Hu, and C.-K. Lee, “Hierarchical graph transformer with adaptive node sampling,” Advances in Neural Information Processing Systems , vol. 35, pp. 21 171–21 183, 2022
2022
Cited alongside, same era.
J. Chen, K. Gao, G. Li, and K. He, “Nagphormer: A tokenized graph transformer for node classification in large graphs,” in The Eleventh International Conference on Learning Representations , 2022
2022
Cited alongside, same era.
K. Guo, X. Cao, Z. Liu, and Y. Chang, “Taming over-smoothing representation on heterophilic graphs,” Information Sciences , vol. 647, p. 119463, 2023
2023
Later among the works it cites.
B. Zhang, G. Feng, Y. Du, D. He, and L. Wang, “A complete expressiveness hierarchy for subgraph gnns via subgraph weisfeiler-lehman tests,” in International Conference on Machine Learning . PMLR, 2023, pp. 41 019–41 077
2023
Later among the works it cites.
W. Zhu, T. Wen, G. Song, X. Ma, and L. Wang, “Hierarchical transformer for scalable graph learning,” in Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence , 2023, pp. 4702–4710
2023
Later among the works it cites.
N. Rathi, I. Chakraborty, A. Kosta, A. Sengupta, A. Ankit, P. Panda, and K. Roy, “Exploring neuromorphic computing based on spiking neural networks: Algorithms to hardware,” ACM Computing Surveys , vol. 55, no. 12, pp. 1–49, 2023
2023
Later among the works it cites.
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Z. Zhou, Y. Zhu, C. He, Y. Wang, Y. Shuicheng, Y. Tian, and L. Yuan, “Spikformer: When spiking neural network meets transformer,” in The Eleventh International Conference on Learning Representations , 2022
2022
Cited alongside, same era.
D. Chen, L. O’Bray, and K. Borgwardt, “Structure-aware transformer for graph representation learning,” in International Conference on Machine Learning . PMLR, 2022, pp. 3469–3489
2022
Cited alongside, same era.
E. Min, Y. Rong, T. Xu, Y. Bian, D. Luo, K. Lin, J. Huang, S. Ananiadou, and P. Zhao, “Neighbour interaction based click-through rate prediction via graph-masked transformer,” in Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval , 2022, pp. 353–362
2022
Cited alongside, same era.
L. Rampášek, M. Galkin, V. P. Dwivedi, A. T. Luu, G. Wolf, and D. Beaini, “Recipe for a general, powerful, scalable graph transformer,” Advances in Neural Information Processing Systems , vol. 35, pp. 14 501–14 515, 2022
2022
Cited alongside, same era.
P. Jia, J. Kou, J. Liu, J. Dai, and H. Luo, “Srfa-grl: Predicting group influence in social networks with graph representation learning,” Information Sciences , vol. 638, p. 118960, 2023
2023
Cited alongside, same era.
Z. Wu, M. Guo, X. Jin, J. Chen, and B. Liu, “Cfago: cross-fusion of network and attributes based on attention mechanism for protein function prediction,” Bioinformatics , vol. 39, no. 3, p. btad123, 2023
2023
Cited alongside, same era.
F. Di Giovanni, L. Giusti, F. Barbero, G. Luise, P. Lio, and M. M. Bronstein, “On over-squashing in message passing neural networks: The impact of width, depth, and topology,” in International Conference on Machine Learning . PMLR, 2023, pp. 7865–7885
2023
Cited alongside, same era.
K. Bose and S. Das, “Can graph neural networks go deeper without over-smoothing? yes, with a randomized path exploration!” IEEE Transactions on Emerging Topics in Computational Intelligence , 2023
2023
Cited alongside, same era.
Q. Wang, T. Zhang, M. Han, Y. Wang, D. Zhang, and B. Xu, “Complex dynamic neurons improved spiking transformer network for efficient automatic speech recognition,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 37, no. 1, 2023, pp. 102–109
2023
Later among the works it cites.
R. Pope, S. Douglas, A. Chowdhery, J. Devlin, J. Bradbury, J. Heek, K. Xiao, S. Agrawal, and J. Dean, “Efficiently scaling transformer inference,” Proceedings of Machine Learning and Systems , vol. 5, 2023
2023
Later among the works it cites.
Y. Zheng, J. Ding, F. Liu, and D. Wang, “Adaptive neural decision tree for eeg based emotion recognition,” Information Sciences , vol. 643, p. 119160, 2023
2023
Later among the works it cites.
W. Zhao, T. Guo, X. Yu, and C. Han, “A learnable sampling method for scalable graph neural networks,” Neural Networks , vol. 162, pp. 412–424, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
Y. Sun, D. Zhu, H. Du, and Z. Tian, “Mhnf: Multi-hop heterogeneous neighborhood information fusion graph representation learning,” IEEE Transactions on Knowledge and Data Engineering , vol. 35, no. 7, pp. 7192–7205, 2023
2023
Later among the works it cites.
J. Li, Z. Yu, Z. Zhu, L. Chen, Q. Yu, Z. Zheng, S. Tian, R. Wu, and C. Meng, “Scaling up dynamic graph representation learning via spiking neural networks,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 37, no. 7, 2023, pp. 8588–8596
2023
Later among the works it cites.
M. Yao, J. Hu, Z. Zhou, L. Yuan, Y. Tian, B. Xu, and G. Li, “Spike-driven transformer,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
Closest in time.
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,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
Closest in time.