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https://doi.org/10.20532/cit.2017.1003492

Concordance-based Kendall's Correlation for Computationally-Light vs. Computationally-Heavy Centrality Metrics: Lower Bound for Correlation

Natarajan Meghanathan ; Department of Computer Science, College of Science, Engineering and Technology, Jackson State University, Jackson, MS, USA

Puni tekst: engleski, pdf (2 MB) str. 103-132 preuzimanja: 304* citiraj
APA 6th Edition
Meghanathan, N. (2017). Concordance-based Kendall's Correlation for Computationally-Light vs. Computationally-Heavy Centrality Metrics: Lower Bound for Correlation. Journal of computing and information technology, 25 (2), 103-132. https://doi.org/10.20532/cit.2017.1003492
MLA 8th Edition
Meghanathan, Natarajan. "Concordance-based Kendall's Correlation for Computationally-Light vs. Computationally-Heavy Centrality Metrics: Lower Bound for Correlation." Journal of computing and information technology, vol. 25, br. 2, 2017, str. 103-132. https://doi.org/10.20532/cit.2017.1003492. Citirano 29.03.2020.
Chicago 17th Edition
Meghanathan, Natarajan. "Concordance-based Kendall's Correlation for Computationally-Light vs. Computationally-Heavy Centrality Metrics: Lower Bound for Correlation." Journal of computing and information technology 25, br. 2 (2017): 103-132. https://doi.org/10.20532/cit.2017.1003492
Harvard
Meghanathan, N. (2017). 'Concordance-based Kendall's Correlation for Computationally-Light vs. Computationally-Heavy Centrality Metrics: Lower Bound for Correlation', Journal of computing and information technology, 25(2), str. 103-132. https://doi.org/10.20532/cit.2017.1003492
Vancouver
Meghanathan N. Concordance-based Kendall's Correlation for Computationally-Light vs. Computationally-Heavy Centrality Metrics: Lower Bound for Correlation. Journal of computing and information technology [Internet]. 2017 [pristupljeno 29.03.2020.];25(2):103-132. https://doi.org/10.20532/cit.2017.1003492
IEEE
N. Meghanathan, "Concordance-based Kendall's Correlation for Computationally-Light vs. Computationally-Heavy Centrality Metrics: Lower Bound for Correlation", Journal of computing and information technology, vol.25, br. 2, str. 103-132, 2017. [Online]. https://doi.org/10.20532/cit.2017.1003492

Sažetak
We identify three different levels of correlation (pair-wise relative ordering, network-wide ranking and linear regression) that could be assessed between a computationally-light centrality metric and a computationally-heavy centrality metric for real-world networks. The Kendall's concordance-based correlation measure could be used to quantitatively assess how well we could consider the relative ordering of two vertices vi and vj with respect to a computationally-light centrality metric as the relative ordering of the same two vertices with respect to a computationally-heavy centrality metric. We hypothesize that the pair-wise relative ordering (concordance)-based assessment of the correlation between centrality metrics is the most strictest of all the three levels of correlation and claim that the Kendall's concordance-based correlation coefficient will be lower than the correlation coefficient observed with the more relaxed levels of correlation measures (linear regression-based Pearson's product-moment correlation coefficient and the network wide ranking-based Spearman's correlation coefficient). We validate our hypothesis by evaluating the three correlation coefficients between two sets of centrality metrics: the computationally-light degree and local clustering coefficient complement-based degree centrality metrics and the computationally-heavy eigenvector centrality, betweenness centrality and closeness centrality metrics for a diverse collection of 50 real-world networks.

Ključne riječi
relative ordering; ranking; linear regression; centrality; correlation

Hrčak ID: 183328

URI
https://hrcak.srce.hr/183328

Posjeta: 399 *