论文标题
具有动态调整图的强大图形注意网络
A Robust graph attention network with dynamic adjusted Graph
论文作者
论文摘要
图形注意力网络（GAT）是处理图数据的有用的深度学习模型。但是，最近的作品表明，经典的GAT容易受到对抗攻击的影响。它在轻微的扰动下急剧下降。因此，如何增强GAT的鲁棒性是一个关键问题。本文提出了强大的GAT（Rogat），以根据注意机制的修订来提高GAT的鲁棒性。与原始的GAT不同，该GAT使用注意机制的不同边缘，但仍然对扰动敏感，Rogat逐渐增加了动态的注意力评分并提高了鲁棒性。首先，Rogat根据平滑度假设修改边缘的重量，这对于普通图很常见。其次，Rogat进一步修改了功能以抑制特征的噪声。然后，动态边缘的重量产生了额外的注意力评分，可用于减少对抗性攻击的影响。针对针对引文数据的针对目标和不靶向攻击的不同实验表明，Rogat的表现优于最近的大多数防御方法。
Graph Attention Networks(GATs) are useful deep learning models to deal with the graph data. However, recent works show that the classical GAT is vulnerable to adversarial attacks. It degrades dramatically with slight perturbations. Therefore, how to enhance the robustness of GAT is a critical problem. Robust GAT(RoGAT) is proposed in this paper to improve the robustness of GAT based on the revision of the attention mechanism. Different from the original GAT, which uses the attention mechanism for different edges but is still sensitive to the perturbation, RoGAT adds an extra dynamic attention score progressively and improves the robustness. Firstly, RoGAT revises the edges weight based on the smoothness assumption which is quite common for ordinary graphs. Secondly, RoGAT further revises the features to suppress features' noise. Then, an extra attention score is generated by the dynamic edge's weight and can be used to reduce the impact of adversarial attacks. Different experiments against targeted and untargeted attacks on citation data on citation data demonstrate that RoGAT outperforms most of the recent defensive methods.
