Open AccessAdvancing Graph Convolution Network with Revised Laplacian Matrix
Author(s) -
Wang Jiahui,
Guo Yi,
Wang Zhihong,
Tang Qifeng,
Wen Xinxiu
Publication year - 2020
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2020.09.015
Subject(s) - laplacian matrix , computer science , preprocessor , graph , convolution (computer science) , theoretical computer science , algorithm , focus (optics) , laplace operator , data mining , artificial intelligence , mathematics , artificial neural network , mathematical analysis , physics , optics
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.