Graph convolutional networks have become a popular tool for learning with graphs and networks. We reflect on the reasons behind the success story.
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References
Kipf, T. N. & Welling, M. in International Conference on Learning Representations https://openreview.net/forum?id=SJU4ayYgl (2017).
Hamilton, W. L., Ying, Z. & Leskovec, J. in Advances in Neural Information Processing Systems 30 (NIPS 2017) 1024–1034 (Conference on Neural Information Processing Systems, 2017).
Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Liò, P. & Bengio, Y. in International Conference on Learning Representations https://openreview.net/forum?id=rJXMpikCZ (2018).
Xu, K., Hu, W., Leskovec, J. & Jegelka S. in International Conference on Learning Representations https://openreview.net/forum?id=ryGs6iA5Km (2019).
Zhang, M. & Chen, Y. in Advances in Neural Information Processing Systems 30 (NIPS 2018) 5171–5181 (Conference on Neural Information Processing Systems, 2018).
Wang, D., Liu, P., Zheng, Y., Qiu, X. & Huang, X. in Association for Computational Linguistics (ACL) 6209–6219 (2020).
Xu, J., Zhu, Z., Zhao, J., Liu, X., Shan, M. & Guo, J. in ACM SIGKDD Conference on Knowledge Discovery and Data Mining(KDD), 3356–3365 (Association for Computing Machinery, 2020).
Emmert-Streib, F., Yli-Harja, O. & Dehmer, M. WIREs Data Mining and Knowledge Discovery 10, e1368 (2020).
Arrieta, A. B. et al. Inf. Fusion 58, 82–115 (2020).
Wu, F., Souza, A. H. Jr, Zhang, T., Fifty, C., Yu, T. & Weinberger, K. Q. in Proc. of the 36th International Conference on Machine Learning (ICML) 6861–6871 (2019).
Dou, Y., Shu, K., Xia, C., Yu, P. S. & Sun, L. in SIGIR '21: Proc. of the 44th International ACM SIGIR Conference on Research and Development in Information 2051–2055 (Association for Computing Machinery, 2021).
Liu, Y., Wang, W., Hu, Y., Hao, J., Chen, X. & Gao, Y. in Proc. of the AAAI Conference on Artificial Intelligence 34, 7211–7218 (Association for the Advancement of Artificial Intelligence, 2020).
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Haghir Chehreghani, M. Half a decade of graph convolutional networks. Nat Mach Intell 4, 192–193 (2022). https://doi.org/10.1038/s42256-022-00466-8
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DOI: https://doi.org/10.1038/s42256-022-00466-8
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