Convex combinations of centrality measures
Document Type
Article
Publication Date
10-2-2021
Abstract
Despite a plethora of centrality measures were proposed, there is no consensus on what centrality is exactly due to the shortcomings each measure has. In this manuscript, we propose to combine centrality measures pertinent to a network by forming their convex combinations. We found that some combinations, induced by regular points, split the nodes into the largest number of classes by their rankings. Moreover, regular points are found with probability 1 and their induced rankings are insensitive to small variation. By contrast, combinations induced by critical points are scarce, but their presence enables the variation in node rankings. We also discuss how optimum combinations could be chosen, while proving various properties of the convex combinations of centrality measures.
Keywords
Centrality measures, Convex combinations, Regular points
Divisions
MathematicalSciences
Funders
Faculty of Science University of Malaya Research Grant (RF008B-2018)
Publication Title
Journal of Mathematical Sociology
Volume
45
Issue
4
Publisher
Taylor & Francis Inc
Publisher Location
530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106 USA