An Auto-Encoding Generative Adversarial Networks for Generating Brain Network

Generative adversarial networks (GANs) are powerful generative models that have led to breakthroughs in image generation. The aim of our work is to adapt the idea of GANs to the graph data based on fMRI, which can solve the issue of limited data and improve the classification performance in the domain of the Autism spectrum disorder (ASD) diagnosis. In this work, we present a data augmentation method that generates synthetic graph data with the proposed graph generation model named α-GCNGAN, which is able to handle the intrinsic challenge of generating brain network with considering flexible context-structure. Extensive experiments on Autism Brain Imaging Data Exchange (ABIDE) dataset demonstrate the effectiveness and generalizability of α-GCNGAN.