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.
Thomas W. Valente, PhD
Current: École des hautes études en santé publique (Rennes/Paris, France)
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).
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