Deep evidential learning in diffusion convolutional recurrent neural network

Zhiyuan Feng; Kai Qi; Bin Shi; Hao Mei; Qinghua Zheng; Hua Wei; Zhiyuan Feng; Kai Qi; Bin Shi; Hao Mei; Qinghua Zheng; Hua Wei

doi:10.3934/era.2023115

Electronic Research Archive

2023, Volume 31, Issue 4: 2252-2264. doi: 10.3934/era.2023115

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Research article Special Issues

Deep evidential learning in diffusion convolutional recurrent neural network

1.
Xi 'an Jiaotong University, 28 Xianning West Road, Beilin District, Xi 'an, China
2.
New Jersey Institute of Technology, New Jersey, USA

Academic Editor: Qing Tian
† The authors contributed equally to this work.

Received: 27 October 2022 Revised: 06 January 2023 Accepted: 28 January 2023 Published: 23 February 2023

Graph neural networks (GNNs) is applied successfully in many graph tasks, but there still exists a limitation that many of GNNs model do not consider uncertainty quantification of its output predictions. For uncertainty quantification, there are mainly two types of methods which are frequentist and Bayesian. But both methods need to sampling to gradually approximate the real distribution, in contrast, evidential deep learning formulates learning as an evidence acquisition process, which could get uncertainty quantification by placing evidential priors over the original Gaussian likelihood function and training the NN to infer the hyperparameters of the evidential distribution without sampling. So evidential deep learning (EDL) has its own advantage in measuring uncertainty. We apply it with diffusion convolutional recurrent neural network (DCRNN), and do the experiment in spatiotemporal forecasting task in a real-world traffic dataset. And we choose mean interval scores (MIS), a good metric for uncertainty quantification. We summarized the advantages of each method.
- uncertainty quantification,
- evidential deep learning,
- graph neural network,
- spatiotemporal forecasting,
- regression task
Citation: Zhiyuan Feng, Kai Qi, Bin Shi, Hao Mei, Qinghua Zheng, Hua Wei. Deep evidential learning in diffusion convolutional recurrent neural network[J]. Electronic Research Archive, 2023, 31(4): 2252-2264. doi: 10.3934/era.2023115

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Abstract

Graph neural networks (GNNs) is applied successfully in many graph tasks, but there still exists a limitation that many of GNNs model do not consider uncertainty quantification of its output predictions. For uncertainty quantification, there are mainly two types of methods which are frequentist and Bayesian. But both methods need to sampling to gradually approximate the real distribution, in contrast, evidential deep learning formulates learning as an evidence acquisition process, which could get uncertainty quantification by placing evidential priors over the original Gaussian likelihood function and training the NN to infer the hyperparameters of the evidential distribution without sampling. So evidential deep learning (EDL) has its own advantage in measuring uncertainty. We apply it with diffusion convolutional recurrent neural network (DCRNN), and do the experiment in spatiotemporal forecasting task in a real-world traffic dataset. And we choose mean interval scores (MIS), a good metric for uncertainty quantification. We summarized the advantages of each method.

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