  Graph Neural Networks (GNNs) are a class of deep learning models capable of
processing graph-structured data, and they have demonstrated significant
performance in a variety of real-world applications. Recent studies have found
that GNN models are vulnerable to backdoor attacks. When specific patterns
(called backdoor triggers, e.g., subgraphs, nodes, etc.) appear in the input
data, the backdoor embedded in the GNN models is activated, which misclassifies
the input data into the target class label specified by the attacker, whereas
when there are no backdoor triggers in the input, the backdoor embedded in the
GNN models is not activated, and the models work normally. Backdoor attacks are
highly stealthy and expose GNN models to serious security risks. Currently,
research on backdoor attacks against GNNs mainly focus on tasks such as graph
classification and node classification, and backdoor attacks against link
prediction tasks are rarely studied. In this paper, we propose a backdoor
attack against the link prediction tasks based on GNNs and reveal the existence
of such security vulnerability in GNN models, which make the backdoored GNN
models to incorrectly predict unlinked two nodes as having a link relationship
when a trigger appear. The method uses a single node as the trigger and poison
selected node pairs in the training graph, and then the backdoor will be
embedded in the GNN models through the training process. In the inference
stage, the backdoor in the GNN models can be activated by simply linking the
trigger node to the two end nodes of the unlinked node pairs in the input data,
causing the GNN models to produce incorrect link prediction results for the
target node pairs.
