# An Energy-Based View of Graph Neural Networks

**Authors:** John Y. Shin, Prathamesh Dharangutte  
**arXiv:** [2104.13492](https://arxiv.org/abs/2104.13492)  
**Date:** 2021-04-27  
**Summary page:** https://www.summarizepaper.com/fr/paper/2104.13492v2/

## Abstract

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.