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Scalable Rumor Source Detection under Independent Cascade Model in Online Social Networks

机译:在线社交网络中独立级联模型下的可扩展谣言源检测

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This paper studies the problem of identifying rumor source in online social networks in which the spread of information follows the popular Independent Cascade model. In the absence of text information, we develop a monitor based approach to evaluate how likely that a piece of information is actually a rumor. Given the underlying social network structure, a number of monitor nodes are injected into the network whose job is to report the data they receive. Based on observing which of monitors received the information and which did not, we propose a polynomial time algorithm to compute rumor quantifier, a reachability based score for ranking the importance of nodes as the rumor source. Extensive simulation results have shown that, with a reasonable number of monitor nodes and appropriate monitor deployment, our rumor source detection algorithm can recognize rumor source effectively and efficiently.
机译:本文研究了在社交网络中识别谣言来源的问题,其中信息的传播遵循流行的Independent Cascade模型。在没有文本信息的情况下,我们开发了一种基于监视器的方法来评估一条信息实际上是谣言的可能性。给定基本的社交网络结构,将许多监视节点注入到网络中,该网络的工作是报告接收到的数据。基于观察哪些监视器接收到信息,哪些监视器没有接收到信息,我们提出了多项式时间算法来计算谣言量词,这是一种基于可达性的分数,用于对节点作为谣言源的重要性进行排名。大量的仿真结果表明,在合理数量的监控节点和适当的监控部署下,我们的谣言源检测算法可以有效,高效地识别谣言源。

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