International Conference on Advanced Technologies, Computer Engineering and Science (ICATCES 2019), Antalya, Türkiye, 26 - 28 Nisan 2019, ss.342-347
Complex networks are one of the tools commonly used in modeling all kinds of events that are related to each other. The identification of effective nodes in complex networks is an important issue needed for the analysis of complex networks. Degree, closeness and betweenness measures are the most important centrality measures commonly used to analyze networks. As a local metric, degree is relatively simple and less effective, although global measures such as the measure of closeness and betweenness can better define effective nodes. However, there are still some disadvantages and limitations of all of these measures. Degree, closeness and betweenness measures are only the data obtained from the topological structure of the network. However, in determining the centralization of the node, other than the topological structure of the network that affect the formation of the network, but not expressed in the topological structure of the network is also effective. In this study, the node weighting method was developed for the determination of node centralization in the network and compared with the current node centering criteria. The experimental study was conducted on a network of players and played competitions in the Australian Open Tennis Tournament held between 2000-2017. By using criteria such as time, experience and success, the weights of the nodes were calculated and compared with the node centering criteria. The results obtained from the experimental study show that the node weighting method gives successful results in the determination of active nodes in complex networks.