AMBIENTUM BIOETHICA BIOLOGIA CHEMIA DIGITALIA DRAMATICA EDUCATIO ARTIS GYMNAST. ENGINEERING EPHEMERIDES EUROPAEA GEOGRAPHIA GEOLOGIA HISTORIA HISTORIA ARTIUM INFORMATICA IURISPRUDENTIA MATHEMATICA MUSICA NEGOTIA OECONOMICA PHILOLOGIA PHILOSOPHIA PHYSICA POLITICA PSYCHOLOGIA-PAEDAGOGIA SOCIOLOGIA THEOLOGIA CATHOLICA THEOLOGIA CATHOLICA LATIN THEOLOGIA GR.-CATH. VARAD THEOLOGIA ORTHODOXA THEOLOGIA REF. TRANSYLVAN
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Autori: BENCE SZABARI, ATTILA KISS.
DOI: 10.24193/subbi.2020.1.05
Published Online: 2020-06-30
Published Print: 2020-06-30
pp. 59-74
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Betweenness centrality measure is used as a general measure of centrality, which can be applied in many scientific fields like social networks, biological networks, telecommunication networks or even in any area that can be well modelled using complex networks where it is important to identify more influential nodes. In this paper, we propose using different clustering algorithms to improve the computation of betweenness centrality over large networks. The experiments show how to achieve faster evaluation without altering the overall computational complexity.
Keywords and phrases. clustering, complex networks, centrality measures, community detection, parallel computation.
2010 Mathematics Subject Classification. 68U99, 94C15.
