*****  To join INSNA, visit  *****



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,


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

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: (promo code: 28569)

Evaluating Health Promotion Programs:

Network Models of the Diffusion of Innovations:

My personal webpage:

The Empirical Networks Project:

You Tube video on Diffusion of Innovations:


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 ***** 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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