  Link prediction, inferring the undiscovered or potential links of the graph,
is widely applied in the real-world. By facilitating labeled links of the graph
as the training data, numerous deep learning based link prediction methods have
been studied, which have dominant prediction accuracy compared with non-deep
methods. However,the threats of maliciously crafted training graph will leave a
specific backdoor in the deep model, thus when some specific examples are fed
into the model, it will make wrong prediction, defined as backdoor attack. It
is an important aspect that has been overlooked in the current literature. In
this paper, we prompt the concept of backdoor attack on link prediction, and
propose Link-Backdoor to reveal the training vulnerability of the existing link
prediction methods. Specifically, the Link-Backdoor combines the fake nodes
with the nodes of the target link to form a trigger. Moreover, it optimizes the
trigger by the gradient information from the target model. Consequently, the
link prediction model trained on the backdoored dataset will predict the link
with trigger to the target state. Extensive experiments on five benchmark
datasets and five well-performing link prediction models demonstrate that the
Link-Backdoor achieves the state-of-the-art attack success rate under both
white-box (i.e., available of the target model parameter)and black-box (i.e.,
unavailable of the target model parameter) scenarios. Additionally, we testify
the attack under defensive circumstance, and the results indicate that the
Link-Backdoor still can construct successful attack on the well-performing link
prediction methods. The code and data are available at
https://github.com/Seaocn/Link-Backdoor.
