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Monitoring Spatial Segregation in Surface Colonizing Microbial Populations

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

Sinan Aral¹, Lev Muchnik, Arun Sundararajan

¹Information, Operations and Management Sciences Department, Stern School of Business, New York University, Kaufmann Management Center, 44 West 4th Street, New York, NY 10012, USA. sinan@stern.nyu.edu

Proceedings of the National Academy of Sciences of the United States of America

|December 17, 2009

Summary

Homophily, not just peer influence, drives behavior in social networks. This study reveals homophily explains over 50% of perceived contagion, challenging prior network influence models.

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Area of Science:

Social Network Analysis
Computational Social Science
Behavioral Economics

Background:

Node behaviors correlate with social network structure over time.
Assortative mixing and temporal clustering are often attributed to peer influence or social contagion.
Homophily (similarity between linked nodes) can also explain these observed network patterns.

Purpose of the Study:

To develop a framework to distinguish between influence and homophily effects in dynamic networks.
To quantify the relative contributions of influence and homophily to observed behavioral contagion.
To correct for overestimation of peer influence in previous network analysis methods.

Main Methods:

Developed a dynamic matched sample estimation framework.
Applied the framework to a large-scale global instant messaging network (27.4 million users).
Utilized longitudinal data on mobile service application adoption, user behavior, demographics, and geography.

Main Results:

Previous methods overestimated peer influence in product adoption by 300-700%.
Homophily explained over 50% of the perceived behavioral contagion.
Demonstrated the importance of distinguishing homophily from influence in dynamic network analysis.

Conclusions:

Homophily plays a more significant role than previously understood in driving behavioral patterns in social networks.
Accurate understanding of network mechanisms is crucial for interventions in diverse fields like epidemiology, marketing, and public health.
The developed framework provides a robust method for analyzing influence and homophily in dynamic network data.