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I have the following problem:

100 people on Twitter. And 3 Networks between them: Friend & Follower
(FF) connections, AT-Interactions (edge A-->B ~ A writes @B ),
RT-Interactions (edge A-->B A retweets one of B's tweets).

I want to find out how much the centrality of a person in the FF and
AT network influences the centrality in the RT network (aka. people
with high centrality in the RT network are people that have been
retweeted often)

Now I have 2 problems:

Problem 1:

It turns out that there are people that have FF connections  but don't
get mentioned at all and don't get retweeted, therefore their
in-degrees and centrality metrics in the AT and RT networks are 0.
Should I exclude these people from the analysis?
The argument for doing this is similar to the assumptions that have to
hold for e.g. SIENA when modeling network evolution. Here actors also
have to be existing in all panels.

Problem 2:

Is it feasible to to perform such a regression given the nature of the
log-normal or powerlaw distribution of in-degrees or centrality
scores?

Bonus question:

If I were to model these correlation of these networks in pNet and try
to capture some sort of centrality effect, is that possible? Would it
simply be incoming-network ties effect or someting like that?


Best Regards
Thomas Plotkowiak
Research Assistant MCM Institute

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