Advancing Graph Convolution Network with Revised Laplacian Matrix

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.