Optimizing Variational Graph Autoencoder for Community Detection

Variational Graph Autoencoders (VGAE) has recently been a popular framework of choice for learning representations on graphs. Its inception has allowed models to achieve state-of-the-art performances for challenging tasks such as link prediction, rating prediction and node clustering. However, a fundamental flaw exists in Variational Autoencoder (VAE) based approaches. Specifically, the objective function of VAE (reconstruction loss), deviates from its primary objective (i.e clustering). In this paper, we attempt to address this issue by introducing two significant changes to Variational Graph Autoencoder for Community Detection (VGAECD). Firstly, we introduce a simplified graph convolution encoder to increase convergence speed and reduce computational time. Secondly, a dual variational objective is introduced to encourage learning of the primary objective. The outcome is a faster converging model with competitive community detection performance.