  Graph neural networks (GNNs) are an emerging model for learning graph
embeddings and making predictions on graph structured data. However, robustness
of graph neural networks is not yet well-understood. In this work, we focus on
node structural identity predictions, where a representative GNN model is able
to achieve near-perfect accuracy. We also show that the same GNN model is not
robust to addition of structural noise, through a controlled dataset and set of
experiments. Finally, we show that under the right conditions, graph-augmented
training is capable of significantly improving robustness to structural noise.
