Pre-Training Identification of Graph Winning Tickets in Adaptive Spatial-Temporal Graph Neural Networks

Author:

Duan Wenying1ORCID,Fang Tianxiang2ORCID,Rao Hong3ORCID,He Xiaoxi4ORCID

Affiliation:

1. Jiangxi Provincial Key Laboratory of Intelligent Systems and Human-Machine Interaction, Nanchang University, Nanchang, Jiangxi, China

2. Nanchang University, Nanchang, China

3. School of Software, Nanchang University, Nanchang, China

4. Faculty of Science and Technology, University of Macau, Macau, China

Funder

The People's Government of Jiangxi Province

National Natural Science Foundation of China

Publisher

ACM

Reference33 articles.

1. Daniel A.Spielman. [n. d.]. Spectral and Algebraic Graph Theory. Online. http://cs-www.cs.yale.edu/homes/spielman/sagt/sagt.pdf

2. Lei Bai, Lina Yao, Salil S. Kanhere, Xianzhi Wang, and Quan Z. Sheng. 2019. STG2Seq: Spatial-Temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019, Macao, China, August 10--16, 2019, Sarit Kraus (Ed.). ijcai.org, 1981--1987.

3. Lei Bai Lina Yao Can Li Xianzhi Wang and Can Wang. 2020. Adaptive graph convolutional recurrent network for traffic forecasting. In Advances in Neural Information Processing Systems. 17804--17815.

4. Spectral sparsification of graphs

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