How Robust Are Graph Neural Networks to Structural Noise?
Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States), James Fox, Sivasankaran Rajamanickam · 2020
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.