  Graph neural networks are a popular variant of neural networks that work with
graph-structured data. In this work, we consider combining graph neural
networks with the energy-based view of Grathwohl et al. (2019) with the aim of
obtaining a more robust classifier. We successfully implement this framework by
proposing a novel method to ensure generation over features as well as the
adjacency matrix and evaluate our method against the standard graph
convolutional network (GCN) architecture (Kipf & Welling (2016)). Our approach
obtains comparable discriminative performance while improving robustness,
opening promising new directions for future research for energy-based graph
neural networks.
