Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks
Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks
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DOI：
10.1073/pnas.0908800106
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发表时间：
2009-12-22
影响因子：
11.1
通讯作者：
Sundararajan, Arun
中科院分区：
文献类型：
--
作者：
Aral, Sinan;Muchnik, Lev;Sundararajan, Arun
随着时间的推移，节点特征和行为通常与社交网络的结构相关。虽然链接节点之间的这种行为的连续混合和时间聚类的证据被用来支持网络中的同伴影响和社会传染的说法，但同质性也可以解释这种证据。在这里，我们开发了一个动态匹配的样本估计框架，以区分影响和同质性的影响，在动态网络中，我们将此框架应用于全球即时通讯网络的2740万用户，使用数据的日常通过的移动的服务应用程序和用户的纵向行为，人口统计和地理数据。我们发现，以前的方法高估了300- 700%，在这个网络中的产品采用决策的同行影响，同质性解释了超过50%的感知行为传染。这些发现和方法对于我们理解网络中驱动传染的机制以及我们如何在流行病学、市场营销、发展经济学和公共卫生等不同领域传播或对抗传染的知识至关重要。
Node characteristics and behaviors are often correlated with the structure of social networks over time. While evidence of this type of assortative mixing and temporal clustering of behaviors among linked nodes is used to support claims of peer influence and social contagion in networks, homophily may also explain such evidence. Here we develop a dynamic matched sample estimation framework to distinguish influence and homophily effects in dynamic networks, and we apply this framework to a global instant messaging network of 27.4 million users, using data on the day-by-day adoption of a mobile service application and users' longitudinal behavioral, demographic, and geographic data. We find that previous methods overestimate peer influence in product adoption decisions in this network by 300-700%, and that homophily explains >50% of the perceived behavioral contagion. These findings and methods are essential to both our understanding of the mechanisms that drive contagions in networks and our knowledge of how to propagate or combat them in domains as diverse as epidemiology, marketing, development economics, and public health.