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

机译:在线社交网络中的独立级联模型下可扩展的谣言源检测

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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:本文研究了在在线社交网络中识别谣言源的问题,其中信息传播遵循流行的独立级联模型。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。鉴于底层的社交网络结构,将许多监视节点注入到网络中,其作业是报告他们接收的数据。基于观察监视器收到的信息并没有,我们提出了一种计算谣言量化的多项式时间算法,基于可达性的基于可达性的分数,用于将节点的重要性排名为谣言源。广泛的仿真结果表明,具有合理数量的监测节点和适当的监视器部署,我们的谣言源检测算法可以有效且有效地识别谣言源。

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