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Introducing Combined Weights and Centrality Measures To Evaluate Network Topologies

Akanmu, Amidu Akinpelumi Gbolasere (2017) Introducing Combined Weights and Centrality Measures To Evaluate Network Topologies. Doctor of Philosophy (PhD) thesis, University of Kent,. (KAR id:79873)

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Abstract

In this research of network structure analysis, the knowledge of centrality measures is applied to discover or predict a most important actor or node in a network/graph. Problems of energy eciency and sustainability are considered, and also those of allocation of resources. In order to enable an ecient allocation of energy resources to the right path in a distributed network such as obtained in a data center, author ́s network and supply chain network, new measures of centralities are introduced aside from the traditional ones of Closeness, Betweenness, Degree and Eigen-Vector centralities. Mixed-mean centrality, which is based on the generalized degree centrality, was developed as a measure to emphasise the importance of a node in the authorship network and the distributed system of a data centre. Weighted centrality measure when used as against the traditional measures mentioned above was able to make prediction for a Distribution Centre (DC) of a Supply Chain Network with an accuracy of 91.6%. Clique-Structure/Node-weighted centrality measure was able to make a prediction with 66% accuracy, while the Weighted Marking, Clique-Structure/Node-Weighted Centrality made a prediction accuracy of 96.2%. The Top Eigen-Vector Weighted Centrality and Newtonian Gravitational Force were also used to predict the location of distribution centre (DC) in a supply chain network with accuracies of 92.9% and 96.9% respectively.

Item Type: Thesis (Doctor of Philosophy (PhD))
Thesis advisor: Wang, Frank Z.
Uncontrolled keywords: CENTRALITY MEASURES, SOCIAL NETWORK ANALYSIS, NEWTONIAN GRAVITATIONAL FORCES
Divisions: Faculties > Sciences > School of Computing
SWORD Depositor: System Moodle
Depositing User: System Moodle
Date Deposited: 30 Jan 2020 15:24 UTC
Last Modified: 31 Jan 2020 12:03 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/79873 (The current URI for this page, for reference purposes)
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