***** To join INSNA, visit http://www.insna.org *****
Sinan
I agree, experiments are great when you can do them and we have 2 published
RCTs that I think demonstrate contagion effects.
Valente, T. W., Hoffman, B. R., Ritt-Olson, A., Lichtman, K., & Johnson, C.
A. (2003). The effects of a social network method for group assignment
strategies on peer led tobacco prevention programs in schools. American
Journal of Public Health, 93, 1837-1843.
Valente, T. W., Ritt-Olson, A., Unger, J., Stacy, A., Okamoto, J. & Sussman,
S., (2007). Peer acceleration: Effects of a network tailored substance
abuse prevention program among high risk adolescents. Addiction, 102,
1804-1815.
- Tom
From: Social Networks Discussion Forum
[mailto:[log in to unmask]] On Behalf Of Sinan Aral
Sent: Saturday, February 26, 2011 11:03 PM
To: [log in to unmask]
Subject: Re: Measuring contagion in longitudinal behavior data
***** To join INSNA, visit http://www.insna.org *****
Steve,
We have been working on this problem as well. In my opinion, the cleanest
and most reliable way to estimate social contagion or peer effects in
networks is through a randomized trial (see the first paper below, which is
to our knowledge the largest randomized trial of social contagion conducted
to date). You might find the other follow papers interesting as well as they
pertain directly to estimating contagion effects. In the first two papers
there are literature reviews of various prior approaches. The second paper
listed is my comment on Tom's excellent paper with Christophe and Ragu
("Opinion leadership and contagion in new product diffusion") which
discusses the theories and methods estimating contagion effects in some
detail... it is also in press at Marketing Science. Hope these help!
Best
Sinan
Aral, S. & Walker, D. (Forthcoming) “Creating Social Contagion through Viral
Product Design: A Randomized Trial of Peer Influence in Networks.”
Management Science. (conditionally accepted pending minor revisions).
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1564856
Aral, S. (in press). “Identifying Social Influence: A Comment on Opinion
Leadership and Social Contagion in New Product Diffusion.” Marketing
Science.
http://mktsci.journal.informs.org/cgi/reprint/mksc.1100.0596v1
Aral, S., Muchnik, L., & Sundararajan, A. 2009. “Distinguishing Influence
Based Contagion from Homophily Driven Diffusion in Dynamic Networks,”
Proceedings of the National Academy of Sciences, Dec. 22, 2009, vol. 106,
no.51.
http://www.pnas.org/content/early/2009/12/09/0908800106.full.pdf
Sinan Aral
Assistant Professor, NYU Stern School of Business.
Research Affiliate, MIT Sloan School of Management.
Personal Webpage: http://pages.stern.nyu.edu/~saral
SSRN Page: http://ssrn.com/author=110270
WIN Workshop: http://www.winworkshop.net
Twitter: http://twitter.com/sinanaral
On 2/26/2011 4:00 AM, Thomas Valente wrote:
***** To join INSNA, visit http://www.insna.org *****
Steve
The Strange & Tuma 1993 article in AJS calculated 2 terms, infection:
calculated as the number of ego’s alters that adopted an innovation after
ego in the next time period after ego adopted; and susceptibility calculated
as the adoption of ego after his alter adopts. Myers discussed these as
well in a paper on collective violence. I also introduced a term I called
the critical mass (in my 1995 book) which was adoption weighted by the
outdegree of the egos. These are also discussed in the 2005 chapter models
and methods for diffusion and somewhat in my latest book “Social Networks
and Health.” At the macro level you can also calculate rate of diffusion
using curve fitting techniques as discussed in my 1993 paper, covered very
well by Mahajan & Peterson (1985). (And don’t forget the classic Bass
(1969) model.) Of course the best way to estimate contagion is by using the
autoregressive model which is discussed in my 2005 chapter and 2010 book.
There are debates about the statistical validity of this approach but it
is quite versatile and statistical limitations seem to be getting worked
out. The latest example, to my knowledge, is the Iyengar et al. paper due
out in Marketing Science.
Bass, F. M. (1969). A new product growth model for consumer durables.
Management Science, 15, 215-227.Sage.
Iyengar, R., Van den Bulte, C. & Valente, T. W. (in press). Opinion
leadership and contagion in new product diffusion. Marketing Science.
Mahajan, V., & Peterson, R. A. (1985). Models of innovation diffusion.
Newbury Park, CA.
Myers, D. J. (2000). The diffusion of collective violence: Infectiousness,
susceptibility, and mass media networks. American Journal of Sociology, 106,
173-208.
Strang, D., & Tuma, N. B. (1993). Spatial and temporal heterogeneity in
diffusion. American Journal of Sociology, 99, 614-639.
Valente, T. W. (1993). Diffusion of innovations and policy decision-making.
Journal of Communication, 43, 30-41.
Valente, T. W. (1995). Network models of the diffusion of innovations.
Cresskill, NJ: Hampton Press.
Valente, T. W. (2005). Models and methods for innovation diffusion. In P. J.
Carrington, J. Scott, & S. Wasserman (Eds.) Models and methods in social
network analysis. Cambridge, UK: Cambridge University Press.
Valente, T. W. (2005). Social Networks and Health: Models Methods and
Applications. New York: Oxford University Press.
-Tom
Thomas W. Valente, PhD
Current: École des hautes études en santé publique (Rennes/Paris, France)
Usual:
Director, Master of Public Health Program
http://www.usc.edu/medicine/mph/
Professor, Department of Preventive Medicine
Keck School of Medicine
University of Southern California
1000 S. Fremont Ave., #8
Building A Room 5133
Alhambra CA 91803
phone: (626) 457-4139; cell: (626) 429-4123
email: [log in to unmask]
Social Networks and Health: Models, Methods, and Applications:
http://www.oup.com/us (promo code: 28569)
Evaluating Health Promotion Programs: www.oup-usa.org/
Network Models of the Diffusion of Innovations: www.hamptonpress.com
My personal webpage: http://www-hsc.usc.edu/~tvalente/
The Empirical Networks Project: http://ipr1.hsc.usc.edu/networks/
You Tube video on Diffusion of Innovations:
http://www.youtube.com/watch?v=ZG9dAIBd4xQ
From: Social Networks Discussion Forum [mailto:[log in to unmask]] On
Behalf Of Steve Eichert
Sent: Friday, February 25, 2011 8:29 PM
To: [log in to unmask]
Subject: Measuring contagion in longitudinal behavior data
***** To join INSNA, visit http://www.insna.org ***** Hello SOCNET,
I'm looking for books, papers, algorithms, and/or ideas on how best to
measure contagion in a network. We have longitudinal behavior data for all
actors in a directed network and want to calculate the degree of contagion
occurring between all connected nodes. We would like to use the calculated
"contagion score" to identify nodes that we can do further analysis on, as
well as to measure the overall level of contagion in the network. The
longitudinal behavior data we have indicates how much of something the nodes
within the network are using over time. We're interested in better
understanding the algorithms folks are using for "adoption contagion"
(someone who has already adopted influences a non adopter to adopt) as well
as "behavior contagion" (a high user influences those connected to them to
use more).
Thoughts?
Thanks,
Steve
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