Abstract:
Centrality measures, erstwhile popular amongst the sociologists and psychologists, have seen broad and
increasing applications across several disciplines of late. Amongst a plethora of application speci c de nitions
available in the literature to rank the vertices, closeness centrality, betweenness centrality and eigenvector
centrality (page-rank) have been the most important and widely applied ones. Networks where information,
signal or commodities are
owing on the edges, surrounds us. Betweenness centrality comes as a handy tool
to analyze such systems, but betweenness computation is a daunting task in large size networks. In this
paper, we propose an e cient heuristic to determine the betweenness-ordering of k vertices (where k is very
less than the total number of vertices) without computing their exact betweenness indices. The algorithm
is based on a non-uniform node sampling model which is developed based on the analysis of Erdos-Renyi
graphs. We apply our approach to nd the betweenness-ordering of vertices in several synthetic and realworld
graphs. The proposed heuristic results very e cient ordering even when runs for a linear time in the
terms of the number of edges. We compare our method with the available techniques in the literature and
show that our method produces more e cient ordering than the currently known methods.
