Eigenvector centrality and uniform dominant eigenvalue of graph components

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dc.contributor.authorAnguzu, Collins
dc.contributor.authorEngström, Christopher
dc.contributor.authorMango, John Magero
dc.contributor.authorKasumba, Henry
dc.contributor.authorSilvestrov, Sergei
dc.contributor.authorAbola, Benard
dc.date.accessioned2023-05-30T18:58:07Z
dc.date.available2023-05-30T18:58:07Z
dc.date.issued2021
dc.description.abstractEigenvector centrality is one of the outstanding measures of central tendency in graph theory. In this paper we consider the problem of calculating eigenvector centrality of graph partitioned into components and how this partitioning can be used. Two cases are considered; first where the a single component in the graph has the dominant eigenvalue, secondly when there are at least two components that share the dominant eigenvalue for the graph. In the first case we implement and compare the method to the usual approach (power method) for calculating eigenvector centrality while in the second case with shared dominant eigenvalues we show some theoretical and numerical results. Keywords: Eigenvector centrality, power iteration, graph, strongly connected component.en_US
dc.identifier.citationAnguzu, C., Engström, C., Mango, J. M., Kasumba, H., Silvestrov, S., & Abola, B. (2021). Eigenvector Centrality and Uniform Dominant Eigenvalue of Graph Components. arXiv preprint arXiv:2107.09137.https://doi.org/10.48550/arXiv.2107.09137en_US
dc.identifier.urihttps://nru.uncst.go.ug/handle/123456789/8847
dc.language.isoenen_US
dc.publisherarXiv preprint arXiven_US
dc.subjectGraph Componentsen_US
dc.subjectEigenvector Centralityen_US
dc.subjectUniform Dominant Eigenvalueen_US
dc.titleEigenvector centrality and uniform dominant eigenvalue of graph componentsen_US
dc.typeArticleen_US
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