Even though the widespread use of social platforms provides convenience to our daily life, it causes some bad results at the same time. For example, misinformation and personal attack can be spread easily on social networks, which drives us to study how to block the spread of misinformation effectively. Unlike the classical rumor blocking problem, we study how to protect the targeted users from being influenced by rumor, called targeted protection maximization (TPM). It aims to block the least edges such that the expected ratio of nodes in targeted set influenced by rumor is at most $\beta$. Under the IC-model, the objective function of TPM is monotone non-decreasing, but not submodular and not supermodular, which makes it difficult for us to solve it by existing algorithms. In this paper, we propose two efficient techniques to solve TPM problem, called Greedy and General-TIM. The Greedy uses simple Hill-Climbing strategy, and get a theoretical bound, but the time complexity is hard to accept. The second algorithm, General-TIM, is formed by means of randomized sampling by Reverse Shortest Path (Random-RS-Path), which reduces the time consuming significantly. A precise approximation ratio cannot be promised in General-TIM, but in fact, it can get good results in reality. Considering the community structure in networks, both Greedy and General-TIM can be improved after removing unrelated communities. Finally, the effectiveness and efficiency of our algorithms is evaluated on several real datasets.

中文翻译：

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社交网络中的针对性保护最大化

尽管社交平台的广泛使用为我们的日常生活提供了便利，但同时也带来了一些不好的结果。例如，错误信息和人身攻击很容易在社交网络上传播，这促使我们研究如何有效阻止错误信息的传播。与经典的谣言拦截问题不同，我们研究如何保护目标用户免受谣言的影响，称为目标保护最大化（TPM）。它旨在阻止最少的边，使得目标集中受谣言影响的节点的预期比例最多为 $\beta$。在IC模型下，TPM的目标函数是单调不减的，但不是亚模也不是超模，这使得我们很难用现有的算法来解决它。在本文中，我们提出了两种有效的技术来解决 TPM 问题，称为贪婪和通用-TIM。Greedy 使用简单的爬山策略，并得到了一个理论界限，但时间复杂度难以接受。第二种算法General-TIM是通过反向最短路径（Random-RS-Path）随机采样形成的，大大减少了耗时。General-TIM 不能保证精确的逼近比，但实际上它在实际中可以得到很好的结果。考虑到网络中的社区结构，Greedy 和 General-TIM 都可以在删除不相关的社区后得到改进。最后，我们的算法的有效性和效率在几个真实的数据集上进行了评估。是通过反向最短路径（Random-RS-Path）随机采样形成的，大大减少了耗时。General-TIM 无法保证精确的逼近比，但实际上，它可以在现实中得到很好的结果。考虑到网络中的社区结构，Greedy 和 General-TIM 都可以在删除不相关的社区后得到改进。最后，我们的算法的有效性和效率在几个真实的数据集上进行了评估。是通过反向最短路径（Random-RS-Path）随机采样形成的，大大减少了耗时。General-TIM 无法保证精确的逼近比，但实际上，它可以在现实中得到很好的结果。考虑到网络中的社区结构，Greedy 和 General-TIM 都可以在删除不相关的社区后得到改进。最后，我们的算法的有效性和效率在几个真实的数据集上进行了评估。