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To live or to die: Encountering conflict information dissemination over simple networks

机译:生死攸关:通过简单网络传播冲突信息

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In an era of networks in which any individual is connected with one another, such as Internet of Things (IoT) and Online Social Networks (OSNs), the networks are evolving into complex systems, carrying a huge volume of information that may provoke even more. An interesting, yet challenging question is how such information dissemination evolves, that is, to continue or to stop. Specifically, we aim to find out the aftermath of epidemic spreading via individuals and conflicting information dissemination. From a holistic, networking view, it is impossible to take every aspect into accounts for complex networks toward these questions. Therefore, we establish a Susceptible-Infectious-Cured (SIC) propagation model to examine two simple network topologies, clique and star, in terms of extinction time and half-life time of information under controllable, epidemic dynamics. For a network of size n, both theoretical and numerical results suggest that extinction time and half-life time are O(log n) for clique networks, and O(log n) for star networks. More interestingly, given an initial network state I0, the extinction time is constant (O(1)) for cliques, and O(log I0) for stars; while the half-life time is O(log 1/I0) for both clique and star networks, respectively. In addition, we developed a method to estimate the conditional infection count distribution, which indicates the scope of information dissemination.
机译:在一个人与人互联的网络时代,例如物联网(IoT)和在线社交网络(OSN),这些网络正在演变为复杂的系统,承载着大量的信息,这些信息可能会激发更多的信息。 。一个有趣但又具有挑战性的问题是这种信息传播如何发展,即继续还是停止。具体来说,我们的目的是找出通过个人传播和信息传播冲突的流行病的后果。从整体的网络角度来看,不可能将所有方面都考虑到针对这些问题的复杂网络。因此,我们建立了可传染感染治愈(SIC)传播模型,以研究两种简单的网络拓扑,即团簇和星形,它们是在可控的流行病动态下,信息的消灭时间和半衰期。对于大小为n的网络,理论和数值结果均表明,对于集团网络,消光时间和半衰期时间为O(log n / n),对于星形网络为O(log n)。更有趣的是,给定初始网络状态I0,对于团簇,消光时间是常数(O(1)),对于恒星,消光时间是O(log I0)。而集团和星型网络的半衰期分别为O(log 1 / I0)。此外,我们开发了一种估计条件感染计数分布的方法,该方法指示了信息传播的范围。

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