The problem ofcheating on various social platforms is getting more serious and fraud detection is becoming more and more necessary. The existing solutions in this scenario have the problem of not using both important information of user characteristics and association relationships at the same time, or cannot be applied to large scale datasets in reality. Aiming at this problem, this paper attempts the application of improved GraphSAGE algorithm to the anti cheating scenarios on the social platform , the weighted GraphSAGE algorithm is proposed which sampling based on the degree of feature similarity between nodes. The experiments are performed on large scale real world datasets. In the offline experiment, the fraud detection performance is significantly improved compared with the benchmark model and the existing mainstream model. In addition, the convergence process of the model is accelerated. Combining the basic rules in the online, it can achieve high accuracy and recalled two fraud groups undetected before.
