Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.
Digital Object Identifier (DOI) : 10.14569/IJACSA.2014.051217
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 5 Issue 12, 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.
Amidu A. G. Akanmu, Frank Z. Wang and Fred A. Yamoah, “Weighted Marking, Clique Structure and Node-Weighted Centrality to Predict Distribution Centre’s Location in a Supply Chain Management” International Journal of Advanced Computer Science and Applications(IJACSA), 5(12), 2014. http://dx.doi.org/10.14569/IJACSA.2014.051217