  Graph neural networks (GNNs) have been widely used in many real applications,
and recent studies have revealed their vulnerabilities against topology
attacks. To address this issue, existing efforts have mainly been dedicated to
improving the robustness of GNNs, while little attention has been paid to the
detection of such attacks. In this work, we study the victim node detection
problem under topology attacks against GNNs. Our approach is built upon the key
observation rooted in the intrinsic message passing nature of GNNs. That is,
the neighborhood of a victim node tends to have two competing group forces,
pushing the node classification results towards the original label and the
targeted label, respectively. Based on this observation, we propose to detect
victim nodes by deliberately designing an effective measurement of the
neighborhood variance for each node. Extensive experimental results on four
real-world datasets and five existing topology attacks show the effectiveness
and efficiency of the proposed detection approach.
