Advancing Graph Convolution Network with Revised Laplacian Matrix

WANG Jiahui; GUO Yi; WANG Zhihong; TANG Qifeng; WEN Xinxiu

doi:10.1049/cje.2020.09.015

Volume 29 Issue 6

Dec. 2020

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Article Navigation > Chinese Journal of Electronics > 2020 > 29(6): 1134-1140

WANG Jiahui, GUO Yi, WANG Zhihong, et al., “Advancing Graph Convolution Network with Revised Laplacian Matrix,” Chinese Journal of Electronics, vol. 29, no. 6, pp. 1134-1140, 2020, doi: 10.1049/cje.2020.09.015

Citation:

WANG Jiahui, GUO Yi, WANG Zhihong, et al., “Advancing Graph Convolution Network with Revised Laplacian Matrix,” Chinese Journal of Electronics, vol. 29, no. 6, pp. 1134-1140, 2020, doi: 10.1049/cje.2020.09.015

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Advancing Graph Convolution Network with Revised Laplacian Matrix

doi: 10.1049/cje.2020.09.015

1.
East China University of Science and Technology, Shanghai 200237, China;
2.
National Engineering Laboratory for Big Data Distribution and Exchange Technologies, Shanghai 200436, China;
3.
Shanghai Engineering Research Center of Big Data and Internet Audience, Shanghai 200072, China

Funds: This work is supported by the National Key Research and Development Program of China (No.2018YFC0807105), the National Natural Science Foundation of China (No.61462073), and the Science and Technology Committee of Shanghai Municipality (No.17DZ1101003, No.18511106602, No.18DZ2252300).

More Information

Corresponding author: GUO Yi (corresponding author) received the M.S. degree in computer science from Xidian University, Xi'an, China and the Ph.D. degree in computer science from Heriot-Watt University, Edinburgh, Scotland in 2005. He is currently a Professor at East China University of Science and Technology. His research focus is text mining, information extraction, knowledge discovery and business intelligence analysis. Now he is the member of MIEEE MCMI MIET MBCS and APMGMSP/PRINCE2-Practitioner, and he is also a committee member of National Engineering Laboratory for Big Data Distribution and Exchange Technologies. (Email:guoyi@ecust.edu.cn)
Received Date: 2019-10-30
Publish Date: 2020-12-25

Abstract

Abstract

Graph convolution networks are extremely efficient on the graph-structure data, which both consider the graph and feature information. Most existing models mainly focus on redefining the complicated network structure, while ignoring the negative impact of lowquality input data during the aggregation process. This paper utilizes the revised Laplacian matrix to improve the performance of the original model in the preprocessing stage. The comprehensive experimental results testify that our proposed model performs significantly better than other off-the-shelf models with a lower computational complexity, which gains relatively higher accuracy and stability.
- Graph convolution network,
- Clustering,
- Label propagation,
- Laplacian matrix,
- Graph structure,
- Fraud detection

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