Gated variable graph convolutional network for warning of parties in tobacco-related cases

In order to implement the "precision supervision" policy, tobacco companies need to increase the hit rate of tobacco-related cases. Past approaches had lacked research on high-risk parties in tobacco-related cases, which had hindered the improvement of case hit rates. Based on the large amount of historical data stored in tobacco companies, mining accurate warning lists is an effective way to improve case hit rates. After conducting the analysis of high-risk parties features, a gated variable relationship graph convolutional network was proposed to obtain an accurate high-risk parties warning list. Firstly, the gated variable relation graph convolutional network used variable relation graph convolutional network to capture the relationship and key features of the parties. Then, the gated layer was applied to further learn the features. Finally, the learned features were inputted to the Softmax layer to get the classification results, and then an alert list is obtained. Through comparison experiments, the constructed model is proved to be more effective. After a municipal monopoly bureau applied the results of this project, its case hit rate improved from about 0.01% to about 0.5%, which proves that the early warning model can meet the needs of real regulation.