Modelling Spreading Process Induced by Agent Mobility in Complex Networks
Modelling Spreading Process Induced by Agent Mobility in Complex Networks
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DOI：
10.1109/tnse.2017.2764523
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发表时间：
2018-10
影响因子：
6.6
通讯作者：
W. Chai
中科院分区：
文献类型：
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作者：
W. Chai
大多数传统的传染病模型假设以接触为基础的传染过程。我们从这一假设出发，研究了传播媒介在网络中的传播过程。这些代理沿着网络中的特定路径从始发地遍历到目的地，并在此过程中感染它们所经过的站点。我们的工作集中在易感-感染-移除(SIR)流行病模型上，并使用连续时间马尔科夫链分析通过单独考虑每个节点的状态转移来建模这种代理移动性引起的传染机制的影响，而不是大多数传统的传染病方法通常考虑所有节点的平均聚集行为。我们的方法做了一个平均场近似，将复杂度从指数降低到多项式。我们研究了在这种代理辅助的感染传播过程中，网络范围内的特性，如流行阈值以及单个节点的脆弱性。此外，由于感染是双向的，我们给出了代理脆弱性的一阶近似。我们通过对欧洲第二繁忙的地铁系统伦敦地铁网络的案例研究，将我们对代理人流动引起的传播过程的分析与基于接触的流行病模型进行了比较，并使用记录通勤者在该系统中的活动的真实数据集进行了比较。我们强调了基于联系人的传播模式与基于代理的传播模式之间的关键差异。具体地说，我们的模型预测了由于代理的移动而比传统的基于接触的模型更大的传播半径。另一个有趣的发现是，与基于接触的模型相比，位于网络中更中心的节点按比例更容易受到感染，我们的模型没有显示出像我们的模型中那样的严格相关性，即使位于网络的核心，节点也可能不是高度敏感的，反之亦然。
Most conventional epidemic models assume contact-based contagion process. We depart from this assumption and study epidemic spreading process in networks caused by agents acting as carrier of infection. These agents traverse from origins to destinations following specific paths in a network and in the process, infecting the sites they travel across. We focus our work on the Susceptible-Infected-Removed (SIR) epidemic model and use continuous-time Markov chain analysis to model the impact of such agent mobility induced contagion mechanics by taking into account the state transitions of each node individually, as oppose to most conventional epidemic approaches which usually consider the mean aggregated behavior of all nodes. Our approach makes one mean field approximation to reduce complexity from exponential to polynomial. We study both network-wide properties such as epidemic threshold as well as individual node vulnerability under such agent assisted infection spreading process. Furthermore, we provide a first order approximation on the agents’ vulnerability since infection is bi-directional. We compare our analysis of spreading process induced by agent mobility against contact-based epidemic model via a case study on London Underground network, the second busiest metro system in Europe, with real dataset recording commuters’ activities in the system. We highlight the key differences in the spreading patterns between the contact-based versus agent assisted spreading models. Specifically, we show that our model predicts greater spreading radius than conventional contact-based models due to agents’ movements. Another interesting finding is that, in contrast to contact-based model where nodes located more centrally in a network are proportionally more prone to infection, our model shows no such strict correlation as in our model, nodes may not be highly susceptible even located at the heart of the network and vice versa.