Advancing Graph Convolution Network with Revised Laplacian Matrix  

作　　者：WANG Jiahui GUO Yi WANG Zhihong TANG Qifeng WEN Xinxiu 

机构地区：[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

出　　处：《Chinese Journal of Electronics》2020年第6期1134-1140,共7页电子学报（英文版）

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

摘　　要：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.

关 键 词：Graph convolution network CLUSTERING Label propagation Laplacian matrix Graph structure Fraud detection 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]