Graph Defense Diffusion Model

Understand

Graph Neural Networks (GNNs) are highly vulnerable to adversarial attacks, which can greatly degrade their performance.

• Existing graph purification methods attempt to address this issue by filtering attacked graphs.
• However, they struggle to defend effectively against multiple types of adversarial attacks (e.g., targeted attacks and non-targeted attacks) simultaneously due to limited flexibility.
• Additionally, these methods lack comprehensive modeling of graph data, relying heavily on heuristic prior knowledge.