Skip to main content
×
Home
    • Aa
    • Aa

Engineering social contagions: Optimal network seeding in the presence of homophily

  • SINAN ARAL (a1), LEV MUCHNIK (a2) and ARUN SUNDARARAJAN (a3)
Abstract
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.

Copyright
Linked references
Hide All

This list contains references from the content that can be linked to their source. For a full set of references and notes please see the PDF or HTML where available.

E. Abrahamson , & L. Rosenkopf (1997). Social network effects on the extent of innovation diffusion: A computer simulation. Organization Science, 8 (3), 289309.

S. Aral (2011). Identifying social influence: A comment on opinion leadership and social contagion in new product diffusion. Marketing Science, 30 (2), 217223.

S. Aral , L. Muchnik , & A. Sundararajan (2009). Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks. Proceedings of the National Academy of Sciences of the United States of America, 106 (51), 2154421549. doi:10.1073/pnas.0908800106

S. Aral & M. Van Alstyne (2011). The diversity-bandwidth tradeoff. American Journal of Sociology, 117 (1), 90171.

S. Aral , & D. Walker (2011). Creating social contagion through viral product design: A randomized trial of peer influence in networks. Management Science, 57 (9), 16231639.

S. Aral , & D. Walker (2012). Identifying influential and susceptible members of social networks. Science, 337 (6092), 337341. doi:10.1126/science.1215842

E. Bakshy , I. Rosenn , C. Marlow , & L. Adamic (2012b). The role of social networks in information diffusion. Proceedings of the 21st International Conference on World Wide Web (WWW), April 16–20, Lyon, France.

M. Barthélemy , A. Barrat , R. Pastor-Satorras , & A. Vespignani (2004). Velocity and hierarchical spread of epidemic outbreaks in scale-free networks. Physical Review Letters, 92 (17), 178701. doi:10.1103/PhysRevLett.92.178701

F. M. Bass (1969). A new product growth for model consumer durables. Management Science, 15 (5), 215227. doi:10.1287/mnsc.15.5.215

M. H. Becker (1970). Factors affecting diffusion of innovations among health professionals. American Journal of Public Health, 60 (2), 294304.

E. Biyalogorsky , E. Gerstner , & B. Libai (2008). Customer referral management: Optimal reward programs. Marketing Science, 20 (1), 8295.

S. P. Borgatti (2005). Centrality and network flow. Social Networks, 27 (1), 5571. doi:10.1016/j.socnet.2004.11.008

F. A. Buttle (1998). Word of mouth: Understanding and managing referral marketing. Journal of Strategic Marketing, 6 (3), 241254.

D. Centola (2010). The spread of behavior in an online social network experiment. Science, 329 (5996), 11941197.

D. Centola (2011). An experimental study of homophily in the adoption of health behavior. Science, 334 (6060), 12691272.

D. Centola , & M. Macy (2007). Complex contagions and the weakness of long ties. American Journal of Sociology, 113 (3), 702734.

C. Chatfield , & G. J. Goodhardt (1973). A consumer purchasing model with erlang inter-purchase times. Journal of the American Statistical Association, 68, 828835.

N. A. Christakis , & J. H. Fowler (2007). The spread of obesity in a large social network over 32 years. The New England Journal of Medicine, 357 (4), 370379.

R. Cohen , K. Erez , ben-Avraham, D., & S. Havlin (2001). Breakdown of the Internet under intentional attack. Physical Review Letters, 86, 3683685

Y. Dover , J. Goldenberg , & D. Shapira (2012). Network Traces on Penetration: Uncovering Degree Distribution from Adoption Data. Marketing Science, 31 (4), 689712.

N. Eagle , M. Macy , & R. Claxton (2010). Network diversity and economic development. Science, 328 (5981), 10291031.

L. C. Freeman (1979). Centrality in social networks: Conceptual clarification. Social networks, 1, 215239.

A. Galeotti , & S. Goyal (2009). Influencing the influencers: A theory of strategic diffusion. The RAND Journal of Economics, 40 (3), 509532. doi:10.1111/j.1756-2171.2009.00075.x

D. Godes , & D. Mayzlin (2009). Firm-created word-of-mouth communication: Evidence from a field test. Marketing Science, 28 (4), 721739.

J. Goldenberg , S. Han , D. R. Lehmann , & J. W. Hong (2009). The role of hubs in the adoption process. Journal of Marketing, 73 (2), 113.

J. Goldenberg , B. Libai , & E. Muller (2001). Talk of the network: A complex systems look at the underlying process of word-of-mouth. Marketing Letters, 12 (3), 211223.

R. Iyengar , C. Van den Bulte , & T. W. Valente (2011). Opinion leadership and social contagion in new product diffusion. Marketing Science, 30 (195).

M. Kitsak , L. K. Gallos , S. Havlin , F. Liljeros , L. Muchnik , H. E. Stanley , & H. A. Makse (2010). Identification of influential spreaders in complex networks. Nature Physics, 6 (11), 888893. doi:10.1038/nphys1746

V. E. Krebs (2002). Uncloaking terrorist networks. First Monday, 7 (4). Retrieved from http://www.firstmonday.dk/issues/issue7_4/krebs/index.html.

D. Lazer , A. Pentland , L. Adamic , S. Aral , A.-L. Barabási , D. Brewer ,. . . M. V. Alstyne (2009). Computational social science. Science, 323 (5915), 721723. doi:10.1126/science.1167742

S. Leider , M. M. Mobius , T. Rosenblat , & Q.A. Do (2009). Directed altruism and enforced reciprocity in social networks. Quarterly Journal of Economics, 124 (4), 18151851.

J. Leskovec , L. A. Adamic , & B. A. Huberman (2007). The dynamics of viral marketing. ACM Transactions on the Web, 1 (1).

B. Libai , E. Biyalogorsky , & E. Gerstner (2003). Setting referral fees in affiliate marketing. Journal of Service Research, 5 (4), 303315. doi:10.1177/1094670503005004003

D. Liben-Nowell , & J. Kleinberg (2008). Tracing information flow on a global scale using internet chain-letter data. Proceedings of the National Academy of Sciences of the United States of America, 105 (12), 46334638.

M. McPherson , L. Smith-Lovin , & J. M. Cook (2001). Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27, 415444. doi:10.1146/annurev.soc.27.1.415

M. E. J. Newman (2003). The structure and function of complex networks. SIAM Review, 45, 167256.

E. Noel , & B. Nyhan (2011). The “unfriending” problem: The consequences of homophily in friendship retention for causal estimates of social influence. Social Networks, 33 (3), 211218.

R. Pastor-Satorras , & A. Vespignani (2001). A. Epidemic spreading in scale-free networks. Physical Review Letters, 86, 3203203.

P. H. Reingen , & J. B. Kernan (1986). Analysis of referral networks in marketing: Methods and illustration. Journal of Marketing Research, 23 (4), 370378.

M. Rosvall , & C. T. Bergstrom (2008). Maps of random walks on complex networks reveal community structure. Proceedings of the National Academy of Sciences of the United States of America, 105 (4), 11181123. doi:10.1073/pnas.0706851105

P. Schmitt , B. Skiera & C. Van den Bulte (2011). Referral programs and customer value. Journal of Marketing, 75 (1), 4659. doi:10.1509/jmkg.75.1.46

J. Singh (1990). Voice, exit, and negative word-of-mouth behaviors: An investigation across three service categories. Journal of the Academy of Marketing Science, 18 (1), 115. doi:10.1177/009207039001800101

M. Uncles , A. Ehrenberg , & K. Hammond (1995). Patterns of buyer behavior: Regularities, models, and extensions. Marketing Science, 14 (3), G7178. doi:10.1287/mksc.14.3.G71

T. W. Valente (2012). Network interventions. Science, 337 (6090), 4953. doi:10.1126/science.1217330

C. Van den Bulte , & Y. V. Joshi (2007). New product diffusion with influentials and imitators. Marketing Science, 26 (3), 400421. doi:10.1287/mksc.1060.0224

D. J. Watts , & P. S. Dodds (2007). Influentials, networks, and public opinion formation. Journal of Consumer Research, 34, 441458.

Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

Network Science
  • ISSN: 2050-1242
  • EISSN: 2050-1250
  • URL: /core/journals/network-science
Please enter your name
Please enter a valid email address
Who would you like to send this to? *
×

Keywords:

Type Description Title
WORD
Supplementary Materials

Aral Supplementary Material
Appendix

 Word (598 KB)
598 KB