Advancing Graph Convolution Network with Revised Laplacian Matrix. Issue 6 (1st November 2020)

• Record Type: Journal Article
• Authors: Wang, Jiahui; Guo, Yi; Wang, Zhihong; Tang, Qifeng; Wen, Xinxiu
• Is Part Of: Chinese journal of electronics. Volume 29:Issue 6(2020)
• Page Start: 1134
• Page End: 1140
• Publication Date: 2020-11-01
• Subjects: computational complexity -- convolutional neural nets -- data structures -- graph theory -- matrix algebra; graph‐structure data -- feature information -- graph convolution network -- network structure -- computational complexity -- Laplacian matrix; Graph convolution network -- Clustering -- Label propagation -- Laplacian matrix -- Graph structure -- Fraud detection; Electronics -- Periodicals; Electronics -- China -- Periodicals; Electronics; China; Periodicals; 621.38105
• DOI: 10.1049/cje.2020.09.015
• Languages: English
• ISSNs: 1022-4653

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.