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Hi Fabrizio,

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 choice.

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 parsing.

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: https://www.stats.ox.ac.uk/~snijders/siena/siena_applications.htm) as well as continuous-time data sets ("Goldfish" by Christoph Stadtfeld & colleagues, see here: http://www.social-networks.ethz.ch/research/goldfish.html). In 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


On 29-11-2017 22:16, Thomas William Valente wrote:
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Fabrizio

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.

See:

https://usccana.github.io/

https://github.com/USCCANA/netdiffuseR

 

-Tom

 

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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.

Fabrizio Marini

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