Social networks allow rapid spread of ideas and innovations while negative information can also propagate widely. When a user receives two opposing opinions, they tend to believe the one arrives first. Therefore, once misinformation or rumor is detected, one containment method is to introduce a positive cascade competing against the rumor. Given a budget $k$, the rumor blocking problem asks for $k$ seed users to trigger the spread of a positive cascade such that the number of the users who are not influenced by rumor can be maximized. The prior works have shown that the rumor blocking problem can be approximated within a factor of $(1-1/e)$ by a classic greedy algorithm combined with Monte Carlo simulation. Unfortunately, the Monte Carlo simulation based methods are time consuming and the existing algorithms either trade performance guarantees for practical efficiency or vice versa. In this paper, we present a randomized approximation algorithm which is provably superior to the state-of-the art methods with respect to running time. The superiority of the proposed algorithm is demonstrated by experiments done on both the real-world and synthetic social networks.

中文翻译：

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在线社交网络谣言拦截的高效随机算法

社交网络允许思想和创新的快速传播，而负面信息也可以广泛传播。当用户收到两种相反的意见时，他们往往会认为第一个到达。因此，一旦检测到错误信息或谣言，一种遏制方法是引入与谣言竞争的正级联。给定预算$千$, 谣言拦截问题要求 $千$种子用户触发正级联的传播，使得不受谣言影响的用户数量可以最大化。先前的工作表明，谣言阻止问题可以在一个因子内近似$(1-1/e)$通过经典的贪心算法结合蒙特卡罗模拟。不幸的是，基于蒙特卡罗模拟的方法非常耗时，现有算法要么用性能保证来换取实际效率，反之亦然。在本文中，我们提出了一种随机近似算法，该算法在运行时间方面优于最先进的方法。在现实世界和合成社交网络上进行的实验证明了所提出算法的优越性。