Auto-encoder based Graph Convolutional Networks for Online Financial Anti-fraud

Abstract

Many practical problems can be formulated as graph-based semi-supervised classification problems. For example, online finance anti-fraud. Recently, many researchers attempt using deep learning methods to solve such problems. In this paper, we propose a novel neural network architecture to perform semi-supervised classification on graph-structured data. We improve the graph convolutional network (GCN) by replacing the graph convolution matrix with auto-encoder module. The proposed neural network is trained by a multi-task objective function. Except the classification task, we train the auto-encoder module to reconstruct the graph convolution matrix. It can be seen as an adaptive spectral convolution on graph. It can increase the depth of neural network without causing over-smooth effect. Additionally, the introduction of reconstruction task can mitigate the cold-start problem. Even the graph topological structure is extreme sparse, our method can learn expressive latent features for vertices. The experimental results show that our method can achieve the state of art performance.