论文标题
优化目标扩散的图形结构
Optimizing Graph Structure for Targeted Diffusion
论文作者
论文摘要
对网络扩散控制的问题已经进行了广泛的研究，其应用程序从营销到控制传染病。但是，在许多应用程序（例如网络安全）中，攻击者可能希望攻击网络的有针对性子图，同时限制对网络其余部分的影响以保持未被发现。我们提出了一种模型药水，其中主要目的是优化图形结构以实现此类目标攻击。我们提出了一种使用基于梯度的方法来利用Rayleigh商和伪摄影理论的算法，用于在大规模求解模型。此外，我们提出了证明目标子图对此类攻击免疫的条件。最后，我们通过对真实和合成网络的实验来证明方法的有效性。
The problem of diffusion control on networks has been extensively studied, with applications ranging from marketing to controlling infectious disease. However, in many applications, such as cybersecurity, an attacker may want to attack a targeted subgraph of a network, while limiting the impact on the rest of the network in order to remain undetected. We present a model POTION in which the principal aim is to optimize graph structure to achieve such targeted attacks. We propose an algorithm POTION-ALG for solving the model at scale, using a gradient-based approach that leverages Rayleigh quotients and pseudospectrum theory. In addition, we present a condition for certifying that a targeted subgraph is immune to such attacks. Finally, we demonstrate the effectiveness of our approach through experiments on real and synthetic networks.
