3 research outputs found
RoGAT: a robust GNN combined revised GAT with adjusted graphs
Graph Neural Networks(GNNs) are useful deep learning models to deal with the
non-Euclid data. However, recent works show that GNNs are vulnerable to
adversarial attacks. Small perturbations can lead to poor performance in many
GNNs, such as Graph attention networks(GATs). Therefore, enhancing the
robustness of GNNs is a critical problem.
Robust GAT(RoGAT) is proposed to improve the robustness of GNNs in this
paper, . Note that the original GAT uses the attention mechanism for different
edges but is still sensitive to the perturbation, RoGAT adjusts the edges'
weight to adjust the attention scores progressively. Firstly, RoGAT tunes the
edges weight based on the assumption that the adjacent nodes should have
similar nodes. Secondly, RoGAT further tunes the features to eliminate
feature's noises since even for the clean graph, there exists some unreasonable
data. Then, we trained the adjusted GAT model to defense the adversarial
attacks. Different experiments against targeted and untargeted attacks
demonstrate that RoGAT outperforms significantly than most the state-of-the-art
defense methods. The implementation of RoGAT based on the DeepRobust repository
for adversarial attacks