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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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