Immunizing a subset of nodes in a network - enabling them to identify and withstand the spread of harmful content - is one of the most effective ways to counter the spread of malicious content. It has applications in network security, public health policy, and social media surveillance. Finding a subset of nodes whose immunization results in the least vulnerability of the network is a computationally challenging task. In this work, we establish a relationship between a widely used network vulnerability measure and the combinatorial properties of networks. Using this relationship and graph summarization techniques, we propose an efficient approximation algorithm to find a set of nodes to immunize. We provide theoretical justifications for the proposed solution and analytical bounds on the runtime of our algorithm. We empirically demonstrate on various real-world networks that the performance of our algorithm is an order of magnitude better than the state of the art solution. We also show that in practice the runtime of our algorithm is significantly lower than that of the best-known solution.

免疫网络中的一部分节点（使它们能够识别并抵御有害内容的传播）是抵御恶意内容传播的最有效方法之一。它在网络安全，公共卫生策略和社交媒体监视中具有应用程序。寻找其免疫导致网络漏洞最少的节点子集是一项计算难题。在这项工作中，我们建立了广泛使用的网络漏洞度量和网络组合属性之间的关系。使用这种关系和图汇总技术，我们提出了一种有效的近似算法来找到一组要免疫的节点。我们为提出的解决方案提供了理论依据，并为算法的运行时间提供了分析界限。我们在各种现实世界的网络上经验证明，算法的性能要比最新解决方案高出一个数量级。我们还表明，在实践中，我们的算法的运行时间明显低于最著名的解决方案的运行时间。