Perturbation-augmented Graph Convolutional Networks: A Graph Contrastive Learning architecture for effective node classification tasks

Author:

Guo Qihang,Yang Xibei,Zhang FengjunORCID,Xu Taihua

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Artificial Intelligence,Control and Systems Engineering

Reference54 articles.

1. Abu-El-Haija, Sami, Kapoor, Amol, Perozzi, Bryan, Lee, Joonseok, 2019a. N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classification. In: Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, UAI 2019, Tel Aviv, Israel, July 22-25, 2019.

2. Abu-El-Haija, Sami, Perozzi, Bryan, Kapoor, Amol, Alipourfard, Nazanin, Lerman, Kristina, Harutyunyan, Hrayr, Steeg, Greg Ver, Galstyan, Aram, 2019b. MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing. In: Proceedings of the 36th International Conference on Machine Learning, ICML 2019, Long Beach, California, USA, 9-15 June, 2019.

3. Gift: Granularity over specific-class for feature selection;Ba;Artif. Intell. Rev.,2023

4. Glee: A granularity filter for feature selection;Ba;Eng. Appl. Artif. Intell.,2023

5. Chen, Ting, Kornblith, Simon, Norouzi, Mohammad, Hinton, Geoffrey E., 2020a. A Simple Framework for Contrastive Learning of Visual Representations. In: Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July, 2020.

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