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Moses,

Thanks for sharing and maintaining a blog.

I often find differences in centrality more interesting. One common approach I use is to plot two measures against each other, such as degree and betweenness. Centrality measures tend to be correlated (on my phone or Id share refs, but there are pubs on this by me, Valente, Contractor). So, when you see a node stand out that is high in betweenness and low in degree, you have evidence of structural holes.

I think of betweenness, closeness, degree, much like median, mean, and mode. All three (six actually) are measures of center. All tell you something a bit different. The differences are interesting (eg income median vs mean). The comparisons are really fascinating.

So, Id encourage you to think what do centrality patterns tell us about the structure than to locate a super node. Just my $0.02.

Ian

Ian McCulloh, PhD
Johns Hopkins University
240-506-3417

From: Moses Boudourides <[log in to unmask]>
Date: Sunday, Apr 01, 2018, 5:20 PM
To: [log in to unmask] <[log in to unmask]>
Subject: [SOCNET] Cumulative Rank Aggregation of a Family of Network Centrality Indices

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Hello everybody,

You might be interested in the following brief report "Cumulative Rank
Aggregation of a Family of Network Centrality Indices" that  I've just
entered in my Medium blog:

https://urldefense.proofpoint.com/v2/url?u=https-3A__medium.com_-40mosabou_cumulative-2Drank-2Daggregation-2Dof-2Da-2Dfamily-2Dof-2Dnetwork-2Dcentrality-2Dindices-2De625a76bf7e4&d=DwIBaQ&c=pZJPUDQ3SB9JplYbifm4nt2lEVG5pWx2KikqINpWlZM&r=uXI5O6HThk1ULkPyaT6h2Ws3RKNKSY__GQ4DuS9UHhs&m=KKhLpuoSdgNSblj5QR8GGUVheVaGB91lw9aMaPeyHVY&s=qiuVA8Rto15u_Dhmq4ASKtLkZOz4f-KZba61k28Bv54&e=

A growing number of centrality indices are used today in social
network analysis. The purpose of using all these network centrality
measures is that through them one might be able to identify the most
important nodes according to a variety of structural criteria (like
nodal degree, closeness, betweenness, eigenvector, PageRank etc.).
Moreover, computations (in Python, R etc. or standalone applications)
may very easily derive the tables of various centrality indices of
network nodes. Therefore, knowing a good deal of network nodal
centralities, the crucial question would be how to make sense for all
such indices in a illuminating way that would account for the
structural features that an empirical network exhibits. What I am
proposing here is a methodology for a cumulative ranking of network
nodes according to the scores that each node possesses, not on a
single centrality measure, but on a whole group (a family) of
centrality measures.

Any remarks, corrections, comments, suggestions etc are more than welcomed.

Best,

--Moses

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