Moody James, Benton Richard A
Department of Sociology, Duke University, Durham, NC; King Abdulaziz University, Jeddah, Makkah, Saudi Arabia.
School of Labor & Employment Relations, University of Illinois at Urbana Champaign, Champaign, IL.
Ann Epidemiol. 2016 Apr;26(4):241-8. doi: 10.1016/j.annepidem.2016.02.011. Epub 2016 Mar 8.
Network diffusion depends on both the pattern and timing of relations, but the relative effects of timing and structure remain unclear. Here, we first show that concurrency (relations that overlap in time) increases epidemic potential by opening new routes in the network. Because this is substantively similar to adding contact paths, we next compare the effects of concurrency by observed levels of path redundancy (structural cohesion) to determine how the features interact.
We establish that concurrency increases exposure analytically and then use simulation methods to manipulate concurrency over observed networks that vary naturally on structural cohesion. This design allows us to compare networks across a wide concurrency range holding constant features that might otherwise conflate concurrency and cohesion. We summarize the simulation results with general linear models.
Our results indicate interdependent effects of concurrency and structural cohesion: although both increase epidemic potential, concurrency matters most when the graph structure is sparse, because the exposure created by concurrency is redundant to observed paths within structurally cohesive networks.
Concurrency works by opening new paths in temporally ordered networks. Because this is substantively similar to having additional observed paths, concurrency in sparse networks has the same effect as adding relations and will have the greatest effect on epidemic potential in sparse networks.
网络传播既取决于关系的模式,也取决于关系的时间安排,但时间安排和结构的相对影响仍不明确。在此,我们首先表明,并发(在时间上重叠的关系)通过在网络中开辟新路径来增加流行潜力。由于这在本质上类似于增加接触路径,我们接下来通过观察到的路径冗余(结构凝聚性)水平来比较并发的影响,以确定这些特征是如何相互作用的。
我们通过分析确定并发会增加接触机会,然后使用模拟方法在结构凝聚性自然变化的观察网络上操纵并发。这种设计使我们能够在保持可能会混淆并发和凝聚性的恒定特征的情况下,比较不同并发范围的网络。我们用一般线性模型总结模拟结果。
我们的结果表明并发和结构凝聚性存在相互依存的影响:虽然两者都会增加流行潜力,但当图结构稀疏时,并发最为重要,因为并发产生的接触机会在结构凝聚的网络中与观察到的路径是冗余的。
并发通过在时间有序的网络中开辟新路径起作用。由于这在本质上类似于拥有额外的观察路径,稀疏网络中的并发与添加关系具有相同的效果,并且对稀疏网络中的流行潜力影响最大。