An Energy-Based View of Graph Neural NetworksDownload PDF

Feb 26, 2021 (edited Apr 25, 2021)EBM_WS@ICLR2021 PosterReaders: Everyone
  • Keywords: energy-based models, graph neural networks, stochastic Langevin gradient descent
  • TL;DR: An energy-based graph neural network is proposed, and its accuracy and robustness is analyzed.
  • 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.
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