Algorithms for recalculating alpha and eigenvector centrality measures using graph partitioning techniquesShow others and affiliations
2022 (English)In: Springer Proceedings in Mathematics and Statistics, Springer Nature, 2022, Vol. 408, p. 541-562Conference paper, Published paper (Refereed)
Abstract [en]
In graph theory, centrality measures are very crucial in ranking vertices of the graph in order of their importance. Alpha and eigenvector centralities are some of the highly placed centrality measures applied especially in social network analysis, disease diffusion networks and mechanical infrastructural developments. In this study we focus on recalculating alpha and eigenvector centralities using graph partitioning techniques. We write an algorithm for partitioning, sorting and efficiently computing these centralities for a graph. We then numerically demonstrate the technique on some sample small-sized networks to recalculate the two centrality measures
Place, publisher, year, edition, pages
Springer Nature, 2022. Vol. 408, p. 541-562
Series
Springer Proceedings in Mathematics & Statistics, ISSN 2194-1009, E-ISSN 2194-1017 ; 408
Keywords [en]
Alpha centrality, Eigenvector centrality, Graph partitioning
National Category
Computational Mathematics Probability Theory and Statistics
Research subject
Mathematics/Applied Mathematics
Identifiers
URN: urn:nbn:se:mdh:diva-61408DOI: 10.1007/978-3-031-17820-7_24Scopus ID: 2-s2.0-85171543141ISBN: 978-3-031-17819-1 (print)OAI: oai:DiVA.org:mdh-61408DiVA, id: diva2:1722911
Conference
SPAS 2019, Västerås, Sweden, September 30–October 2
Funder
Sida - Swedish International Development Cooperation Agency2023-01-012023-01-012023-10-04Bibliographically approved