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Hey,

Thanks Christian for the reference on EpiModel (which I find really
great!), although we are not the authors of it :) (you can thank Samuel
Jenness, Steven M. Goodreau and Martina Morris for it!). That said, which
way to go depends on what are you trying to model. netdiffuseR, which I
maintain, has a bunch of alternatives, whereas you want to run a lagged
regression model (for which you can use our exposure function), do
permutation tests (which are implemented in the struct_test function), or
simulate diffusion processes (which is provided by the rdiffnet and
rdiffnet_multiple functions), you can do it with netdiffuseR. The last
feature, simulating diffusion processes, can also be done in EpiModel, the
main differences, from what I understand, are two: (1) EpiModel is
significantly more flexible and complex, it uses ERGMs and tERGMs to
simulate changes in the networks (which I find very cool) and is written in
a modular way, so the user can define almost all dynamics in the
diffusion/contagion process, whereas netdiffuseR is less flexible and has
some pre-defined dynamics, and (2) Given the previous point, more
flexibility comes with a cost, simulations are slower in EpiModel in
comparison with netdiffuseR; in netdiffuseR simulating thousands of
diffusion networks with thousands of vertices is relatively fast; something
that, again, as far as I understand, is not the case with EpiModel.

HIH


George G. Vega Yon
+1 (626) 381 8171
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On Thu, Nov 30, 2017 at 12:34 AM, Christian Steglich <[log in to unmask]>
wrote:

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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 https://urldefense.proofpoint.com/v2/url?u=http-3A__www.epimodel.org_&d=DwIFaQ&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=rJcmfDTCs_jzPeVP5ngfAkGazMNthtEfOuPUZfDPuxU&s=Z7bJQWEnRo2z4DchOGnou3gVY0toKlli_hqdY2valQ0&e= 
> <https://urldefense.proofpoint.com/v2/url?u=http-3A__www.epimodel.org_&d=DwMD-g&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=50wiMoBhKkE6a7Of9_wlfCT5Mu3DLu5nlEpHgeWDlc4&s=mLEH4APcufQ0-gPO4LSmsmOFrhYUuCGjgBeRE_ZlDgE&e=>,
> 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://urldefense.proofpoint.com/v2/url?u=https-3A__www.stats.ox.ac.uk_-7E&d=DwIFaQ&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=rJcmfDTCs_jzPeVP5ngfAkGazMNthtEfOuPUZfDPuxU&s=4qPbM1qZ7eos8noPylOTfYER7H-eibIUMSS8FRYN2PU&e= 
> snijders/siena/siena_applications.htm
> <https://urldefense.proofpoint.com/v2/url?u=https-3A__www.stats.ox.ac.uk_-7Esnijders_siena_siena-5Fapplications.htm&d=DwMD-g&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=50wiMoBhKkE6a7Of9_wlfCT5Mu3DLu5nlEpHgeWDlc4&s=saU87BCdljg1N0pYwGBF3anRNLE45dfZX33PVU8PjQ8&e=>)
> as well as continuous-time data sets ("Goldfish" by Christoph Stadtfeld &
> colleagues, see here: https://urldefense.proofpoint.com/v2/url?u=http-3A__www.social-2Dnetworks&d=DwIFaQ&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=rJcmfDTCs_jzPeVP5ngfAkGazMNthtEfOuPUZfDPuxU&s=TO55RoagQMIQqq4qbwQ728Hgg7xPqBMAZz08UHYP-Dk&e= .
> ethz.ch/research/goldfish.html
> <https://urldefense.proofpoint.com/v2/url?u=http-3A__www.social-2Dnetworks.ethz.ch_research_goldfish.html&d=DwMD-g&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=50wiMoBhKkE6a7Of9_wlfCT5Mu3DLu5nlEpHgeWDlc4&s=UDmNAX6gaK37KmtJhb5DcA2c-GFAjBfJwDeU7vi0n0Y&e=>).
> 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://urldefense.proofpoint.com/v2/url?u=https-3A__usccana.github.io_&d=DwIFaQ&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=rJcmfDTCs_jzPeVP5ngfAkGazMNthtEfOuPUZfDPuxU&s=D5l3cGNnP_ZOdB34KVHiWzx53GZ-p8tTxLJJCxDpHu0&e= 
> <https://urldefense.proofpoint.com/v2/url?u=https-3A__usccana.github.io_&d=DwMFaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=h3mEjrnTGiVHB-Zq1rVrng&m=YIXow0cUbjZOQDEDtHRBjcBlyvZ4ZEMdGChUQW3I9so&s=SI1XbPmXNuJZWnv5DR2DnwTPMJgaSlKz6BAwkEcj_hg&e=>
>
> https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_USCCANA_netdiffuseR&d=DwIFaQ&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=rJcmfDTCs_jzPeVP5ngfAkGazMNthtEfOuPUZfDPuxU&s=yKXx8_ma_2fmqm63zRfRiQlgVO2rolS57ZMqBH4ORBo&e= 
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>
>
>
> -Tom
>
>
>
> Thomas W. Valente, PhD
>
> Professor and Interim Chair
>
> Department of Preventive Medicine
>
> Keck School of Medicine
>
> University of Southern California
> <https://urldefense.proofpoint.com/v2/url?u=https-3A__maps.google.com_-3Fq-3DSouthern-2BCalifornia-250D-2BSoto-2BStreet-2BBuilding-2C-2BSuite-2B330-250D-2B2001-2BN-2BSoto-2BStreet-26entry-3Dgmail-26source-3Dg&d=DwIFaQ&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=rJcmfDTCs_jzPeVP5ngfAkGazMNthtEfOuPUZfDPuxU&s=yXVgaarcXNRsjRYJ3ww4HolEgQJlu8KQac7qrDlCXGU&e= >
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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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