Revisiting graph neural networks from hybrid regularized graph signal reconstruction

Author:

Miao Jiaxing,Cao FeilongORCID,Ye Hailiang,Li Ming,Yang Bing

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience

Reference44 articles.

1. Abu-El-Haija, S., Perozzi, B., Kapoor, A., Alipourfard, N., Lerman, K., Harutyunyan, H., et al. (2019). Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing. In Proceedings of international conference on machine learning (pp. 21–29). Long Beach, USA.

2. Bachmann, G., Bécigneul, G., & Ganea, O. (2020). Constant curvature graph convolutional networks. In Proceedings of international conference on machine learning (pp. 486–496). Vienna, Austria.

3. Convolutional sparse coding for compressed sensing CT reconstruction;Bao;IEEE Transactions on Medical Imaging,2019

4. Graph neural networks with convolutional arma filters;Bianchi;IEEE Transactions on Pattern Analysis and Machine Intelligence,2021

5. A graph CNN-LSTM neural network for short and long-term traffic forecasting based on trajectory data;Bogaerts;Transportation Research Part C (Emerging Technologies),2020

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