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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=DwID-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_-7Esnijders_siena_siena-5Fapplications.htm&d=DwID-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.ethz.ch_research_goldfish.html&d=DwID-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=DwID-g&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=50wiMoBhKkE6a7Of9_wlfCT5Mu3DLu5nlEpHgeWDlc4&s=I9xtCGf5AyB84bg9oF2VdESx2FR7Rlbz09R97zm0oPA&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=DwID-g&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=50wiMoBhKkE6a7Of9_wlfCT5Mu3DLu5nlEpHgeWDlc4&s=8g2Bgww2sS-BZgXo6-KbVYOQoauDeMlibHJICa1h_kM&e=  
> <https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_USCCANA_netdiffuseR&d=DwMFaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=h3mEjrnTGiVHB-Zq1rVrng&m=YIXow0cUbjZOQDEDtHRBjcBlyvZ4ZEMdGChUQW3I9so&s=VOxBbAgM4IF-h5tq19auiaqq60V_8aGgwL2sh58kLa4&e=>
>
> -Tom
>
> Thomas W. Valente, PhD
>
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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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