University of Hertfordshire

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  • 907295

    Final published version, 916 KB, PDF document

  • Amidu Akanmu
  • Frank Wang
  • Fred Yamoah
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Original languageEnglish
Article number12
Pages (from-to)120-128
Number of pages9
JournalInternational Journal of Advanced Computer Science and Applications
Volume5
Issue12
DOIs
Publication statusPublished - 2014

Abstract

Despite the importance attached to the weights or strengths on the edges of a graph, a graph is only complete if it has both the combinations of nodes and edges. As such, this paper brings to bare the fact that the node-weight of a graph is also a critical factor to consider in any graph/network’s
evaluation, rather than the link-weight alone as commonly considered. In fact, the combination of the weights on both the nodes and edges as well as the number of ties together contribute effectively to the measure of centrality for an entire graph or network, thereby clearly showing more information. Two
methods which take into consideration both the link-weights and node-weights of graphs (the Weighted Marking method of prediction of location and the Clique/Node-Weighted centrality measures) are considered, and the result from the case studies shows that the clique/node-weighted centrality measures give an accuracy of 18% more than the weighted marking method, in the
prediction of Distribution Centre location of the Supply Chain Management

Notes

This is an open access article distributed under the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits unrestricted non-commerical use, distribution, and reproduction in any medium, provided the original work is properly cited.

ID: 9314635