  Graph Neural Networks (GNNs) have demonstrated significant application
potential in various fields. However, GNNs are still vulnerable to adversarial
attacks. Numerous adversarial defense methods on GNNs are proposed to address
the problem of adversarial attacks. However, these methods can only serve as a
defense before poisoning, but cannot repair poisoned GNN. Therefore, there is
an urgent need for a method to repair poisoned GNN. In this paper, we address
this gap by introducing the novel concept of model repair for GNNs. We propose
a repair framework, Repairing Robustness of Graph Neural Networks via Machine
Unlearning (GraphMU), which aims to fine-tune poisoned GNN to forget
adversarial samples without the need for complete retraining. We also introduce
a unlearning validation method to ensure that our approach effectively forget
specified poisoned data. To evaluate the effectiveness of GraphMU, we explore
three fine-tuned subgraph construction scenarios based on the available
perturbation information: (i) Known Perturbation Ratios, (ii) Known Complete
Knowledge of Perturbations, and (iii) Unknown any Knowledge of Perturbations.
Our extensive experiments, conducted across four citation datasets and four
adversarial attack scenarios, demonstrate that GraphMU can effectively restore
the performance of poisoned GNN.
