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Dynamic link prediction (DLP) makes graph prediction based on historicalinformation. Since most DLP methods are highly dependent on the training datato achieve satisfying prediction performance, the quality of the training datais crucial. Backdoor attacks induce the DLP methods to make wrong prediction bythe malicious training data, i.e., generating a subgraph sequence as thetrigger and embedding it to the training data. However, the vulnerability ofDLP toward backdoor attacks has not been studied yet. To address the issue, wepropose a novel backdoor attack framework on DLP, denoted as Dyn-Backdoor.Specifically, Dyn-Backdoor generates diverse initial-triggers by a generativeadversarial network (GAN). Then partial links of the initial-triggers areselected to form a trigger set, according to the gradient information of theattack discriminator in the GAN, so as to reduce the size of triggers andimprove the concealment of the attack. Experimental results show thatDyn-Backdoor launches successful backdoor attacks on the state-of-the-art DLPmodels with success rate more than 90%. Additionally, we conduct a possibledefense against Dyn-Backdoor to testify its resistance in defensive settings,highlighting the needs of defenses for backdoor attacks on DLP.

Dyn-Backdoor: Backdoor Attack on Dynamic Link Prediction