A Restricted Black-box Adversarial Framework Towards Attacking Graph Embedding Models

Understand

With the great success of graph embedding model on both academic and industry area, the robustness of graph embedding against adversarial attack inevitably becomes a central problem in graph learning domain.

• Regardless of the fruitful progress, most of the current works perform the attack in a white-box fashion: they need to access the model predictions and labels to construct their adversarial loss.
• However, the inaccessibility of model predictions in real systems makes the white-box attack impractical to real graph learning system.
• This paper promotes current frameworks in a more general and flexible sense -- we demand to attack various kinds of graph embedding model with black-box driven.