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首页> 外文会议>International conference on neural information processing;Annual conference of Asia-Pacific Neural Network Society >DARIM: Dynamic Approach for Rumor Influence Minimization in Online Social Networks
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DARIM: Dynamic Approach for Rumor Influence Minimization in Online Social Networks

机译：DARIM：在线社交网络中将谣言影响最小化的动态方法

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摘要

This paper investigates the problem of rumor influence minimization in online social networks (OSNs). Over the years, researchers have proposed strategies to diminish the influence of rumor mainly divided into two well-known methods, namely the anti-rumor campaign strategy and the blocking nodes strategy. Although these strategies have proven to be efficient in different scenarios, their gaps remain in other situations. Therefore, we introduce in this work the dynamic approach for rumor influence minimization (DARIM) that aims to overcome these shortcomings and exploit their advantage. The objective is to find a compromise between the blocking nodes and anti-rumor campaign strategies that minimize the most the influence of a rumor. Accordingly, we present a solution formulated from the perspective of a network inference problem by exploiting the survival theory. Thus, we introduce a greedy algorithm based on the likelihood principle. Since the problem is NP-hard, we prove the objective function is submodular and monotone and provide an approximation within (1 — 1/e) of the optimal solution. Experiments performed in real multiplex and single OSNs provide evidence about the performance of the proposed algorithm compared the work of literature.
机译：本文研究了在线社交网络（OSN）中的谣言影响最小化问题。多年来，研究人员提出了减少谣言影响的策略，主要分为两种众所周知的方法，即反谣言运动策略和阻止结点策略。尽管已证明这些策略在不同情况下是有效的，但在其他情况下仍存在差距。因此，我们在这项工作中引入了一种动态化的谣言影响最小化方法（DARIM），旨在克服这些缺点并利用它们的优势。目的是在阻止节点和反谣言竞选策略之间找到折衷办法，以最大程度地减少谣言的影响。因此，我们提出了一种利用生存理论从网络推理问题的角度制定的解决方案。因此，我们引入了一种基于似然原理的贪婪算法。由于问题是NP难题，因此我们证明目标函数是亚模和单调的，并提供了最佳解的（1/1 / e）以内的近似值。在实际的多路复用和单OSN中执行的实验提供了与文献工作相比所提出的算法性能的证据。

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