Convex combinations of centrality measures
Bibliographic Data
| ID | 10142113 |
|---|---|
| Authors | Ying Ying Keng (0000-0001-9054-3321, University of Malaya), Kiam Heong Kwa (0000-0001-8813-8626, University of Malaya, corresponding author), C J McClain (West Virginia University Institute of Technology), Christopher McClain (West Virginia University Institute of Technology) |
| Year | 2020 |
| Volume | 45 |
| Issue | 4 |
| Pages | 195-222 |
| Publication date | 2020-07-27 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of Mathematical Sociology (JOURNAL) |
| Journal identifiers | ISSN: 0022-250X • E-ISSN: 1545-5874 |
| Publisher | Taylor & Francis (PUBLISHER • GB) |
| DOI | 10.1080/0022250x.2020.1765776 |
| OpenAlex | W3045547046 |
| Language | EN |
| References cited | 27 |
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
Centrality · Combinatorics · Contrast (vision · Data mining · Measure (data warehouse · Node (physics · Physics · Regular polygon · Artificial Intelligence · Complex Network Analysis Techniques · Computer Science · Game Theory and Applications · Mathematics
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| Highly cited | No |
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