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.
Highlights
Centrality measures are an important tool in social and complex network analysis to quantify the eminence of nodes
We provide a comparison of traditional centrality measures: degree (DC), closeness (CC), betweenness (BC) and, eigenvector (EC) and the proposed hybrid centrality measure [Harmonically attenuated-betweenness centrality (HABC), Exponentially attenuated-betweenness centrality (EABC) ( α = 0.5 ), EABC ( α = 0.75 )] in the considered networks
We provide a comparison of traditional centrality measures: degree (DC), closeness (CC), betweenness (BC) and, eigenvector (EC) and the proposed hybrid centrality measure (HADC, Exponentially attenuated degree centrality (EADC)(α = 0.25), EADC(α = 0.5), EADC(α = 0.75)) in the considered networks
Summary
Centrality measures are an important tool in social and complex network analysis to quantify the eminence of nodes. Afterwards, new hybridization of centrality measures are introduced for solving two complex computational problems in networks based on the definitions of node-weighted centrality measures.
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