Robust Meta Network Embedding against Adversarial Attacks

Yang Zhou, Jiaxiang Ren, Dejing Dou, Ruoming Jin, Jingyi Zheng, Kisung Lee

Abstract: Recent studies have shown that graph mining models are vulnerable to adversarial attacks. This paper proposes a robust meta network embedding framework, RoMNE, which improves the robustness of multiple network embedding on adversarial noisy networks while preserving the utility on original clean ones. First, we propose a generic meta learning based multiple network embedding model that can quickly adapt it to new embedding tasks on a variety of network data with only a small number of parameter and training updates. Second, Gumbel estimator and Gaussian smoothing techniques are introduced to implement differentiable approximation for optimizing non-differential objective of effective adversarial attacks. Last but not least, the adversarial attack and defense models are integrated into a dynamic adversarial training model. The competition of two models helps the latter be robust to adversarial attacks.