How Robust Are Graph Neural Networks to Structural Noise?

Understand

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.