Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks.

ABSTRACT: 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

SUBMITTER: Aral S 

PROVIDER: S-EPMC2799846 | biostudies-literature | 2009 Dec

REPOSITORIES: biostudies-literature