Jointly Attacking Graph Neural Network and its Explanations. (arXiv:2108.03388v2 [cs.LG] UPDATED)

Graph Neural Networks (GNNs) have boosted the performance for many
graph-related tasks. Despite the great success, recent studies have shown that
GNNs are highly vulnerable to adversarial attacks, where adversaries can
mislead the GNNs' prediction by modifying graphs. On the other hand, the
explanation of GNNs (GNNExplainer) provides a better understanding of a trained
GNN model by generating a small subgraph and features that are most influential
for its prediction. In this paper, we first perform empirical studies to
validate …

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