Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks
Open accessAbstract
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.
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- Authors
- Aral, S., Muchnik, L., & Sundararajan, A.
- Affiliations
- New York University (US)
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2009-12-22
- Volume
- 106
- Issue
- 51
- Pages
- 21544-21549
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC2799846
- OpenAlex
- W2149910108
- Cited by
- 1,207
- References
- 43
- Citations by year
- 2026: 19 · 2025: 39 · 2024: 52 · 2023: 48 · 2022: 48 · 2021: 76 · 2020: 68 · 2019: 82 · 2018: 106 · 2017: 94 · 2016: 106 · 2015: 88 · 2014: 105 · 2013: 107 · 2012: 69
- FWCI
- 38.31
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- Funders
- National Science Foundation (0953832, CAREER)
- York University
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Opinion Dynamics and Social Influence
- Social Sciences › Decision Sciences › Management Science and Operations Research › Innovation Diffusion and Forecasting
- Keywords
- social contagion, homophily, peer influence, dynamic networks, assortative mixing, mobile application adoption
- MeSH terms
- Communication, Decision Making, Humans, Peer Group, Social Support, Models, Statistical
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