SPP-CNN: An Efficient Framework for Network Robustness Prediction-Reference-Cited by-同舟云学术

SPP-CNN: An Efficient Framework for Network Robustness Prediction

Published:2023-10 Issue:10 Volume:70 Page:4067-4079
ISSN:1549-8328
Container-title:IEEE Transactions on Circuits and Systems I: Regular Papers
language:
Short-container-title:IEEE Trans. Circuits Syst. I

Author:

Wu Chengpei¹^ORCID,Lou Yang²^ORCID,Wang Lin³^ORCID,Li Junli¹^ORCID,Li Xiang⁴^ORCID,Chen Guanrong⁵^ORCID

Affiliation:

1. College of Computer Science, Sichuan Normal University, Chengdu, China

2. Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan

3. Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai, China

4. Department of Control Science and Engineering, Institute of Complex Networks and Intelligent Systems, Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University, Shanghai, China

5. Department of Electrical Engineering, City University of Hong Kong, Hong Kong, China

Funder

National Natural Science Foundation of China

Foundation of Key Laboratory of System Control and Information Processing, Ministry of Education, China

Hong Kong Shun Hing Education and Charity Fund

Fundamental Research Funds for the Central Universities

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Hardware and Architecture

Link

http://xplorestaging.ieee.org/ielx7/8919/10268488/10197169.pdf?arnumber=10197169

Reference68 articles.

1. Controllability of Deep-Coupling Dynamical Networks

2. Visualizing and Understanding Convolutional Networks

3. Advances in Network Controllability

4. Deep Residual Learning for Image Recognition

5. Measuring and Improving Communication Robustness of Networks

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