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

Sinan Aral*, Lev Muchnik, Arun Sundararajan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

853 Scopus citations

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 300-700%, 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.

Original languageAmerican English
Pages (from-to)21544-21549
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Volume106
Issue number51
DOIs
StatePublished - 22 Dec 2009
Externally publishedYes

Keywords

  • Dynamic matching estimation
  • Identification
  • Peer influence
  • Social networks

Fingerprint

Dive into the research topics of 'Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks'. Together they form a unique fingerprint.

Cite this