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Identifying multiple infection sources in a network

机译:识别网络中的多个感染源

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Estimating which nodes are the infection sources that introduce a virus or rumor into a network, or the locations of pollutant sources, plays a critical role in limiting the potential damage to the network through timely quarantine of the sources. In this paper, we derive estimators for the infection sources and their infection regions based on the infection network geometry. We show that in a geometric tree with at most two sources, our estimator identifies these sources with probability going to one as the number of infected nodes increases. We extend and generalize our methods to general graphs, where the number of infection sources are unknown and there may be multiple sources. Numerical results are presented to verify the performance of our proposed algorithms under different types of graph structures.
机译:估计哪些节点是将病毒或谣言引入网络的感染源,或者污染物源的位置,对于通过及时隔离这些源来限制对网络的潜在损害起着至关重要的作用。在本文中,我们根据感染网络的几何形状推导了感染源及其感染区域的估计量。我们表明,在最多包含两个来源的几何树中,我们的估计器会随着感染节点数量的增加而将这些来源的概率提高到一个。我们将方法扩展并推广到一般图,在这些图中,感染源的数量未知,并且可能有多个感染源。数值结果表明,可以验证我们提出的算法在不同类型的图结构下的性能。

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