In this work, we consider misinformation propagating through a social network and study the problem of its prevention. In this problem, a "bad" campaign starts propagating from a set of seed nodes in the network and we use the notion of a limiting (or "good") campaign to counteract the effect of misinformation. The goal is to identify a set of $k$ users that need to be convinced to adopt the limiting campaign so as to minimize the number of people that adopt the "bad" campaign at the end of both propagation processes. This work presents \emph{RPS} (Reverse Prevention Sampling), an algorithm that provides a scalable solution to the misinformation mitigation problem. Our theoretical analysis shows that \emph{RPS} runs in $O((k + l)(n + m)(\frac{1}{1 - \gamma}) \log n / \epsilon^2 )$ expected time and returns a $(1 - 1/e - \epsilon)$-approximate solution with at least $1 - n^{-l}$ probability (where $\gamma$ is a typically small network parameter and $l$ is a confidence parameter). The time complexity of \emph{RPS} substantially improves upon the previously best-known algorithms that run in time $\Omega(m n k \cdot POLY(\epsilon^{-1}))$. We experimentally evaluate \emph{RPS} on large datasets and show that it outperforms the state-of-the-art solution by several orders of magnitude in terms of running time. This demonstrates that misinformation mitigation can be made practical while still offering strong theoretical guarantees.

中文翻译：

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社交网络中减少错误信息的反向预防抽样

在这项工作中，我们考虑通过社交网络传播的错误信息并研究其预防问题。在这个问题中，“坏”活动开始从网络中的一组种子节点传播，我们使用限制（或“好”）活动的概念来抵消错误信息的影响。目标是确定一组需要被说服采用限制活动的 $k$ 用户，以便在两个传播过程结束时最大限度地减少采用“坏”活动的人数。这项工作提出了 \emph{RPS}（反向预防采样），这是一种为错误信息缓解问题提供可扩展解决方案的算法。我们的理论分析表明 \emph{RPS} 在 $O((k + l)(n + m)(\frac{1}{1 - \gamma}) \log n / \epsilon^2 )$ 预期时间内运行并返回一个 $(1 - 1/e - \epsilon)$-近似解，概率至少为 $1 - n^{-l}$（其中 $\gamma$ 是典型的小网络参数，$l$ 是置信度范围）。\emph{RPS} 的时间复杂度大大改进了以前最知名的算法，这些算法在 $\Omega(mnk \cdot POLY(\epsilon^{-1}))$ 中运行。我们在大型数据集上对 \emph{RPS} 进行了实验评估，并表明它在运行时间方面比最先进的解决方案好几个数量级。这表明减少错误信息是可行的，同时仍然提供强有力的理论保证。我们在大型数据集上对 \emph{RPS} 进行了实验评估，并表明它在运行时间方面比最先进的解决方案好几个数量级。这表明减少错误信息是可行的，同时仍然提供强有力的理论保证。我们在大型数据集上对 \emph{RPS} 进行了实验评估，并表明它在运行时间方面比最先进的解决方案好几个数量级。这表明减少错误信息是可行的，同时仍然提供强有力的理论保证。