Graph generative adversarial network

Abstract

In this report, we briefly explain the building blocks of Generative Adversarial Network (GAN), recent research on generalization of Convolution Neural Network (CNN) to graphs, and experimented on further usage of graph convolution on other types of model. We also proposed a simple method to upsample graphs. Experiments include usage of graph convolution on Variational Autoencoder (VAE) and GAN. Comparison of result between traditional and proposed graph VAE are observed showing graph VAE achieving better performance. An experimentation on Graph GAN shows the model is unable to converge. Problem analysis and idea for improvement on graph convolution and upsampling are given.