Dear all,
I am trying to simulate a series of networks that have characteristics
similar to real life social networks. Specifically I am interested in
networks that have (a) a reasonable degree of clustering (as measured by
the transitivity function in igraph) and (b) a reasonable degree of degree
polarization (as measured by the average degree of the top 10% nodes with
highest degree divided by the overall average degree).
Right now I am using two functions from irgaph (sample_pa and
sample_smallworld) but these are not ideal since they only allow me to vary
one of the two characteristics. Either the network has good clustering but
not enough polarization or the other way round.
I looked around and I found some network algorithms that solve the problem
(E.g., Jackson and Rogers, Meeting Strangers and Friends of Friends), but I
did not find their implemented in an R package. I also found the R package
NetSim which seems to be in this spirit, but I cannot get it to work.
Could anyone point me to an R library that I could check out? I do not care
much about the specific algorithm used as long as it allows me to vary
clustering and degree polarization in certain ranges.
Thanks,
Michael
Michael Haenlein
Professor of Marketing
ESCP Europe, Paris
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