Are Graph Convolutional Networks With Random Weights Feasible?-Reference-Cited by-同舟云学术

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Are Graph Convolutional Networks With Random Weights Feasible?

Published:2022 Issue: Volume: Page:1-18
ISSN:0162-8828
Container-title:IEEE Transactions on Pattern Analysis and Machine Intelligence
language:
Short-container-title:IEEE Trans. Pattern Anal. Mach. Intell.

Author:

Huang Changqin¹^ORCID,Li Ming¹^ORCID,Cao Feilong²^ORCID,Fujita Hamido³^ORCID,Li Zhao⁴^ORCID,Wu Xindong⁵

Affiliation:

1. Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University, Jinhua, China

2. College of Sciences, China Jiliang University, Hangzhou, China

3. HUTECH University, Ho Chi Minh City, Vietnam

4. Link2Do Technology Ltd, Hangzhou, China

5. Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China), Hefei University of Technology, Hefei, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Artificial Intelligence,Computational Theory and Mathematics,Computer Vision and Pattern Recognition,Software

Link

http://xplorestaging.ieee.org/ielx7/34/4359286/09796468.pdf?arnumber=9796468

Reference92 articles.

1. Fast Haar Transforms for Graph Neural Networks

2. Image Super-Resolution via Adaptive $\ell _{p} (0<p<1)$ Regularization and Sparse Representation

4. LanczosNet: Multi-scale deep graph convolutional networks;liao;Proc Int Conf Learn Representations,2019

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