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Engineering Social Contagions: Optimal Network Seeding in the Presence of Homophily

Forthcoming in Network Science

44 Pages Posted: 27 Feb 2011 Last revised: 20 Feb 2013

Sinan Aral

Massachusetts Institute of Technology (MIT) - Sloan School of Management

Lev Muchnik

Independent

Arun Sundararajan

New York University (NYU): Stern School of Business and Center for Urban Science+Progress

Date Written: February 18, 2013

Abstract

We use data on a real, large-scale social network of 27 million individuals interacting daily, together with the day-by-day adoption of a new mobile service product, to inform, build and analyze data-driven simulations of the effectiveness of seeding (network targeting) strategies under different social conditions. Three main results emerge from our simulations. First, failure to consider homophily creates significant overestimation of the effectiveness of seeding strategies, casting doubt on conclusions drawn by simulation studies that do not model homophily. Second, seeding is constrained by the small fraction of potential influencers that exist in the network. We find that seeding more than 0.2% of the population is wasteful because the gain from their adoption is lower than the gain from their natural adoption (without seeding). Third, seeding is more effective in the presence of greater social influence. Stronger peer influence creates a greater than additive effect when combined with seeding. Our findings call into question some conventional wisdom about these strategies and suggest that their overall effectiveness may be overestimated.

Suggested Citation

Aral, Sinan and Muchnik, Lev and Sundararajan, Arun, Engineering Social Contagions: Optimal Network Seeding in the Presence of Homophily (February 18, 2013). Forthcoming in Network Science. Available at SSRN: https://ssrn.com/abstract=1770982 or http://dx.doi.org/10.2139/ssrn.1770982

Sinan Aral (Contact Author)

Massachusetts Institute of Technology (MIT) - Sloan School of Management ( email )

100 Main Street
E62-416
Cambridge, MA 02142
United States

Lev Muchnik

Independent ( email )

No Address Available

Arun Sundararajan

New York University (NYU): Stern School of Business and Center for Urban Science+Progress ( email )

44 West 4th Street, KMC 8-90
New York, NY 10012
United States

HOME PAGE: http://oz.stern.nyu.edu/

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