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• W4366779419 startingPage "102008" @default.
• W4366779419 abstract "Nowadays, it is imperative to identify the combination of profitable products (nodes) within large product networks. Data exploration for such broad-spectrum product networks require sophisticated techniques for their analysis and meaningful inferences. The most commonly used techniques are the centrality metrics due to their efficiency in computation. Centrality metrics include Degree Centrality (DC), Closeness Centrality (CC), Betweenness Centrality (BC), Eigenvector Centrality (EVC), Katz Centrality (KC), and the local clustering coefficient-dependent degree centrality (LCCDC or LD). In this research, a novel approach the global clustering coefficient-dependent degree centrality (GCCDC or GD) method has been formulated for the analysis of links of the profitable products (nodes) in a large product network. GCCDC or GD is formulated by using the global clustering coefficient method which is efficient and accurate for large product networks such as product Amazon network. Furthermore, three correlation coefficients that are Pearson’s, Spearman’s, and Kendall’s have been used for evaluation. The results have shown that GD is preferable over LD to avoid uncertainties in computation of results for real-world datasets. To prove the scalability of the novel method, a dataset from different domain (biological yeast protein-protein interaction (PPI) dataset) was also analyzed using similar metrics and shown improved results." @default.
• W4366779419 created "2023-04-25" @default.
• W4366779419 creator A5020847809 @default.
• W4366779419 creator A5058360648 @default.
• W4366779419 creator A5067199092 @default.
• W4366779419 date "2023-06-01" @default.
• W4366779419 modified "2023-09-25" @default.
• W4366779419 title "A novel global clustering coefficient-dependent degree centrality (GCCDC) metric for large network analysis using real-world datasets" @default.
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• W4366779419 doi "https://doi.org/10.1016/j.jocs.2023.102008" @default.
• W4366779419 hasPublicationYear "2023" @default.
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