Skip to main content
SpringerLink
Log in
Book cover

International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction

SBP 2012: Social Computing, Behavioral - Cultural Modeling and Prediction pp 214–221Cite as

Using Mixed-Mode Networks to Disentangle Multiple Sources of Social Influence

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7227))

Abstract

Social network analysis has been widely used to model various forms of network influence on individual behavior. In network studies into peer influence on adolescent risk-taking health behavior, either friendship influence (using one-mode networks) or affiliation-based peer influence (using two-mode networks) have been employed to examine potential sources of network influence on adolescent substance use behavior. However, these sources of peer influence may be potentially confounded with each other. This paper introduces a new network method of incorporating one network’s influence into another network’s influence patterns, enabling us to decompose the different sources of network influences though the use of a combination of the networks. Specifically, the method incorporates affiliation-based peer influence (operationalized by a two-mode network) with either a second two-mode network, or the peer influence (operationalized by a one-mode network). Empirical examples are included to demonstrate the utility of this new approach.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications. Cambridge University Press, New York (1994)

    Google Scholar 

  2. Alexander, C., Piazza, M., Mekos, D., Valente, T.W.: Peers, schools, and adolescent cigarette smoking. Journal of Adolescent Health 29(1), 22–30 (2001)

    Article  Google Scholar 

  3. Cleveland, H.H., Wiebe, R.P.: The moderation of adolescent-to-peer similarity in tobacco and alcohol use by school levels of substance use. Child Development 74(1), 279–291 (2003)

    Article  Google Scholar 

  4. Crosnoe, R., Muller, C., Frank, K.A.: Peer context and the consequences of adolescent drinking. Social Problems 51(2), 288–304 (2004)

    Article  Google Scholar 

  5. Ennett, S.T., Bauman, K.E., Hussong, A., Faris, R., Foshee, V.A., Cai, L., et al.: The peer context of adolescent substance use: Findings from social network analysis. Journal of Research on Adolescence 16(2), 159–186 (2006)

    Article  Google Scholar 

  6. Urberg, K.A., Degirmencioglu, S.M., Pilgrim, C.: Close friend and group influence on adolescent cigarette smoking and alcohol use. Developmental Psychology 33(5), 834–844 (1997)

    Article  Google Scholar 

  7. Fujimoto, K., Chih-Ping, C., Valente, T.W.: The network autocorrelation model using two-mode network Data: Affiliation exposure and biasness in ρ. Social Networks 33(3), 231–243 (2011)

    Article  Google Scholar 

  8. Fujimoto, K., Unger, J., Valente, T.: Network method of measuring affiliation-based peer influence: Assessing the influences on teammates smokers on adolescent smoking. Child Development (in press)

    Google Scholar 

  9. Fujimoto, K., Valente, T.W.: Alcohol peer influence from participating in organized school activities among U.S. adolescents: A network approach (manuscript)

    Google Scholar 

  10. Marsden, P., Friedkin, N.E.: Network studies of social influence. Sociological Method & Re-search 22(1), 127–151 (1993)

    Article  Google Scholar 

  11. Burt, R.S.: Social contagion and innovation: Cohesion versus structural equivalence. American Journal of Sociology 92, 1287–1335 (1987)

    Article  Google Scholar 

  12. Valente, T.W.: Network models of the diffusion of innovations. Hampton Press, Cresskill (1995)

    Google Scholar 

  13. McPherson, J.M.: Hypernetwork sampling: duality and differentiation among voluntary organizations. Social Networks 3, 225–249 (1982)

    Article  Google Scholar 

  14. Doreian, P., Teuter, K., Wang, C.-S.: Network autocorrelation models: Some Monte Carlo re-sults. Sociological Methods and Research 13, 155–200 (1984)

    Article  Google Scholar 

  15. Doreian, P.: Linear models with spatially distributed data. Sociological Methods and Research 9, 29–60 (1980)

    Article  Google Scholar 

  16. Dow, M.M.: A bi-parametric approach to network autocorrelation: Galton’s problem. Sociological Methods and Research 13, 201–217 (1984)

    Article  Google Scholar 

  17. Ord, K.: Estimation methods for models of spatial interaction. Journal of the American Statistical Association 70, 120–126 (1975)

    Article  MathSciNet  MATH  Google Scholar 

  18. Leenders, R.A.J.: Modeling social influence through network autocorrelation: Constructing the weight matrix. Social Networks 24, 21–47 (2002)

    Article  Google Scholar 

  19. Fujimoto, K., Soto, D., Valente, T.W.: A network approach to decompose peer influences from crowds and organized sports (manuscript)

    Google Scholar 

  20. Harris, K.M.: The National Longitudinal Study of Adolescent Health (Add Health), Wave I & II, 1994-1996; Wave III, 2001-2002; Wave IV, 2007-2009 [machine-readable data file and documentation]. Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC (2009)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Division of Health Promotion & Behavioral Sciences, School of Public Health, University of Texas, Health Science Center at Houston, USA

    Kayo Fujimoto

Authors
  1. Kayo Fujimoto
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Engineering, Rochester Institute of Technology, 14623, Rochester, New York, USA

    Shanchieh Jay Yang

  2. Applied Physics Laboratory, Research and Exploratory Development Department, Johns Hopkings University, 20723, Laurel, MD, USA

    Ariel M. Greenberg

  3. SA Technologies, 3750 Paladian Viillage Drive, Building 600, 30066, Marietta, GA, USA

    Mica Endsley

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Fujimoto, K. (2012). Using Mixed-Mode Networks to Disentangle Multiple Sources of Social Influence. In: Yang, S.J., Greenberg, A.M., Endsley, M. (eds) Social Computing, Behavioral - Cultural Modeling and Prediction. SBP 2012. Lecture Notes in Computer Science, vol 7227. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29047-3_26

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-29047-3_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29046-6

  • Online ISBN: 978-3-642-29047-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation