first and foremost, the triple links between data, modelling, and
theory are crucial to get right before proceeding to any analysis.
To illustrate: If your data consists of time-stamped e-mail
traffic, you should probably not apply methods that require
aggregation of data over hard-to-define time windows, because it
implies a tremendous loss of data and relevant detail about timing
(what came first, what came later in reaction to it). Or, if your
theory doesn't address network dynamics but your data includes
dynamically shifting network links, something is missing in the
theory framework. Or, if ideas can be adopted and un-adopted
again, a model that only considers adoption might not be the best
In this triple linking, everything that misses or mismatches
needs to be filled in by *assumptions that are typically hard to
sell, and that people therefore tend not to write about -
omissions that undermine trustworthiness and make a lot of
contagion research hard to follow, not completely 'non sequitur'
but getting close, while leaving fast readers with less doubts
than they should have. It is better to be as explicit as possible
about assumptions, and think a lot before proceeding to data
That being said... next to the excellent reference Tom Valente
gave ("epimodel" software http://www.epimodel.org/, and George
Vega Yon's work) there are agent-based modelling techniques (e.g.,
Netlogo-applications, which can be more or less easily calibrated
to empirical data), *assuming dynamic or static underlying
networks of empirical or theoretically *assumed topologies...
The approach that I myself am a bit familiar with is stochastic
actor-based (or actor-oriented) modelling, for which there are
software packages for discrete-time data ("RSiena" by Tom
Snijders; see a list of papers applying the method here:
as well as continuous-time data sets ("Goldfish" by Christoph
Stadtfeld & colleagues, see here:
this approach, the main *assumption is "agency", i.e., that social
actors propel network change by taking decisions to rewire their
own ties, and they are the ones deciding about whether or not to
adopt an idea, given the information at their disposal. Often,
this actor-orientedness helps constructing a walkable bridge
between analysis method and social science theory.
All the best, Christian
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You can use our R library netdiffuseR to model diffusion and contagion through a wide variety of theoretical processes.
There are graphing, simulation, statistical test procedures, and you tube videos of tutorials.
Thomas W. Valente, PhD
Professor and Interim Chair
Department of Preventive Medicine
Keck School of Medicine
University of Southern California
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Hello, I'm totally new to Social Network Analysis.
I have a dataset of a corporate network consisting of 3000 actors and I have the evolution over time of the network.
My data indicates the diffusion of the political ideas of the actors.
I'm looking for references to know which would be the best approach and which software to use to model a contagion of ideas and to study network effects.
Thanks in advance.
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