Spatiotemporal graph convolutional recurrent networks for traffic matrix prediction-Reference-Cited by-同舟云学术

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Spatiotemporal graph convolutional recurrent networks for traffic matrix prediction

Published:2020-07-30 Issue:11 Volume:31 Page:
ISSN:2161-3915
Container-title:Transactions on Emerging Telecommunications Technologies
language:en
Short-container-title:Trans Emerging Tel Tech

Author:

Zhao Jianlong¹^ORCID,Qu Hua¹²,Zhao Jihong²³,Dai Huijun²,Jiang Dingchao²

Affiliation:

1. School of Software Engineering Xi'an Jiaotong University Xi'an China

2. School of Electronic and Information Engineering Xi'an Jiaotong University Xi'an China

3. School of Telecommunication and Information Engineering Xi'an University of Posts and Telecommunications Xi'an China

Funder

National Nature Science Foundation of China

National Science and Technology Major Project of the Ministry of Science and Technology of China

Publisher

Wiley

Subject

Electrical and Electronic Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/ett.4056

Reference41 articles.

1. Machine Learning for Networking: Workflow, Advances and Opportunities

2. State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems

3. ShaoW IannoneL RougierJL DevienneF VisteM. Scalable BGP prefix selection for effective inter‐domain traffic engineering. Paper presented at: Proceedings of the 15th IEEE/IFIP Network Operations and Management Symposium (NOMS);2016:Istanbul Turkey.

4. A survey of methods for encrypted traffic classification and analysis

5. Deep learning-based network application classification for SDN

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