  Graph neural networks (GNNs) have achieved tremendous success, but recent
studies have shown that GNNs are vulnerable to adversarial attacks, which
significantly hinders their use in safety-critical scenarios. Therefore, the
design of robust GNNs has attracted increasing attention. However, existing
research has mainly been conducted via experimental trial and error, and thus
far, there remains a lack of a comprehensive understanding of the vulnerability
of GNNs. To address this limitation, we systematically investigate the
adversarial robustness of GNNs by considering graph data patterns,
model-specific factors, and the transferability of adversarial examples.
Through extensive experiments, a set of principled guidelines is obtained for
improving the adversarial robustness of GNNs, for example: (i) rather than
highly regular graphs, the training graph data with diverse structural patterns
is crucial for model robustness, which is consistent with the concept of
adversarial training; (ii) the large model capacity of GNNs with sufficient
training data has a positive effect on model robustness, and only a small
percentage of neurons in GNNs are affected by adversarial attacks; (iii)
adversarial transfer is not symmetric and the adversarial examples produced by
the small-capacity model have stronger adversarial transferability. This work
illuminates the vulnerabilities of GNNs and opens many promising avenues for
designing robust GNNs.
