References
Abadal S, Jain A, Guirado R, López-Alonso J, Alarcón E. Computing graph neural networks: a survey from algorithms to accelerators. ACM Computing Surveys, 2022, 54(9): 191
Zhang M H, Cui Z C, Neumann M, Chen Y X. An end-to-end deep learning architecture for graph classification. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence and 30th Innovative Applications of Artificial Intelligence Conference and Eighth AAAI Symposium on Educational Advances in Artificial Intelligence. 2018, 544
Verma S, Zhang Z L. Graph capsule convolutional neural networks. 2018, arXiv preprint arXiv: 1805.08090
Sabour S, Frosst N, Hinton G E. Dynamic routing between capsules. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. 2017, 3859–3869
Zhang X Y, Chen L H. Capsule graph neural network. In: Proceedings of International Conference on Learning Representations. 2019
Acknowledgements
This work was supported by the National Natural Science Foundation of China (Grant Nos. 62141214 and 62272171).
Author information
Authors and Affiliations
Corresponding author
Additional information
Supporting information
The supporting information is available online at journa.hep.com.cn and link.springer.com.
Rights and permissions
About this article
Cite this article
Wu, S., Xiong, Y. & Weng, C. Dynamic depth-width optimization for capsule graph convolutional network. Front. Comput. Sci. 17, 176346 (2023). https://doi.org/10.1007/s11704-023-2483-4
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s11704-023-2483-4