  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 that GNNExplainer can act as an inspection tool and have the potential
to detect the adversarial perturbations for graphs. This finding motivates us
to further initiate a new problem investigation: Whether a graph neural network
and its explanations can be jointly attacked by modifying graphs with malicious
desires? It is challenging to answer this question since the goals of
adversarial attacks and bypassing the GNNExplainer essentially contradict each
other. In this work, we give a confirmative answer to this question by
proposing a novel attack framework (GEAttack), which can attack both a GNN
model and its explanations by simultaneously exploiting their vulnerabilities.
Extensive experiments on two explainers (GNNExplainer and PGExplainer) under
various real-world datasets demonstrate the effectiveness of the proposed
method.
