Information, Operations and Management Sciences Department, Stern School of Business, New York University, Kaufmann Management Center, 44 West 4th Street, New York, NY 10012, USA.
Proc Natl Acad Sci U S A. 2009 Dec 22;106(51):21544-9. doi: 10.1073/pnas.0908800106. Epub 2009 Dec 10.
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.
节点的特征和行为通常与随时间推移的社交网络结构相关。虽然这种关联节点之间行为的混合和时间聚类的证据被用于支持网络中同伴影响和社会传染的说法，但同质性也可能解释这种证据。在这里，我们开发了一个动态匹配样本估计框架，以区分动态网络中的影响和同质性效应，并将该框架应用于 2740 万用户的全球即时通讯网络，使用有关移动服务应用程序的日常采用以及用户的纵向行为、人口统计和地理数据。我们发现，在这个网络中，以前的方法高估了产品采用决策中的同伴影响，高达 300-700%，而同质性解释了 >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在流行病学、营销、发展经济学和公共卫生等各个领域传播或对抗它们的知识都是至关重要的。
