Denoising Aggregation of Graph Neural Networks by Using Principal Component Analysis-Reference-Cited by-同舟云学术

Denoising Aggregation of Graph Neural Networks by Using Principal Component Analysis

Author:

Dong Wei¹^ORCID,Woźniak Marcin²^ORCID,Wu Junsheng³^ORCID,Li Weigang³^ORCID,Bai Zongwen⁴^ORCID

Affiliation:

1. School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an, China

2. Faculty of Applied Mathematics, Silesian University of Technology, Gliwice, Poland

3. School of Software, Northwestern Polytechnical University, Xi’an, China

4. Shaanxi Key Laboratory of Intelligent Processing of Big Energy Data, School of Physics and Electronic Information, Yanan University, Yan’an, China

Funder

National Natural Science Foundation of China

Key Research and Development Program of Yanan

Rector of the Silesian University of Technology, Gliwice, Poland

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Information Systems,Control and Systems Engineering

Link

Reference21 articles.

1. FastGCN: Fast learning with graph convolutional networks via importance sampling;chen;Proc Int Conf Learn Representations,0

2. Inductive representation learning on large graphs;hamilton;Proc Int Conf Neural Inf Process,0

3. Graph attention networks;veli?kovi?;Proc Int Conf Learn Representations,0

4. Stochastic training of graph convolutional networks with variance reduction;chen;Proc Int Conf Mach Learn,0

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