Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.