Abstract:
Centrality measures have been proved to be a salient computational science tool
for analyzing networks in the last two to three decades aiding many problems in the
domain of computer science, economics, physics, and sociology. With increasing complexity and vividness in the network analysis problems, there is a need to modify the
existing traditional centrality measures. Weighted centrality measures usually consider
weights on the edges and assume the weights on the nodes to be uniform. One of the
main reasons for this assumption is the hardness and challenges in mapping the nodes
to their corresponding weights. In this paper, we propose a way to overcome this kind
of limitation by hybridization of the traditional centrality measures. The hybridization
is done by taking one of the centrality measures as a mapping function to generate
weights on the nodes and then using the node weights in other centrality measures
for better complex ranking.
