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How Overlapping Community Structure Affects Epidemic Spreading in Complex Networks

机译:重叠的社区结构如何影响复杂网络中的流行病传播

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Many real-world networks exhibit overlapping community structure in which vertices may belong to more than one community. It has been recently shown that community structure plays an import role in epidemic spreading. However, the effect of different vertices on epidemic behavior was still unclear. In this paper, we classify vertices into overlapping and non-overlapping ones, and investigate in detail how they affect epidemic spreading respectively. We propose a SIR epidemic model named ICP-SIR (Inner-Community Preferred Susceptible-Infective-Recovered) where the inner-community and inter-community spreading rates are different. We consider the case where epidemic process is started by immunizing and infecting multiple overlapping or non-overlapping vertices. The epidemic model is applied on both synthetic and real-world networks. Simulation results indicate that compared to non-overlapping vertices, overlapping vertices play a vital role in spreading the epidemic across communities. The result of our research may provide some reference on epidemic immunization in the future.
机译:许多现实世界的网络都显示出重叠的社区结构,其中顶点可能属于多个社区。最近显示,社区结构在流行病传播中起重要作用。但是,尚不清楚不同顶点对流行病行为的影响。在本文中,我们将顶点分为重叠和不重叠的顶点,并详细研究它们分别如何影响流行性传播。我们提出了一个SIR流行病模型,称为ICP-SIR(内部社区首选易感感染恢复),其中内部社区和社区间的传播率不同。我们考虑了通过免疫和感染多个重叠或非重叠顶点来开始流行过程的情况。该流行病模型适用于合成网络和现实网络。模拟结果表明,与非重叠顶点相比,重叠顶点在跨社区传播该流行病中起着至关重要的作用。我们的研究结果可能为将来的流行病免疫提供参考。

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