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Computationally Light vs. Computationally Heavy Centrality Metrics: Correlation Analysis Between Computationally Light and Computationally Heavy Centrality Metrics

Computationally Light vs. Computationally Heavy Centrality Metrics: Correlation Analysis Between Computationally Light and Computationally Heavy Centrality Metrics

Copyright: © 2018 |Pages: 32
ISBN13: 9781522538028|ISBN10: 152253802X|EISBN13: 9781522538035
DOI: 10.4018/978-1-5225-3802-8.ch002
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MLA

Natarajan Meghanathan. "Computationally Light vs. Computationally Heavy Centrality Metrics: Correlation Analysis Between Computationally Light and Computationally Heavy Centrality Metrics." Centrality Metrics for Complex Network Analysis: Emerging Research and Opportunities, IGI Global, 2018, pp.34-65. https://doi.org/10.4018/978-1-5225-3802-8.ch002

APA

N. Meghanathan (2018). Computationally Light vs. Computationally Heavy Centrality Metrics: Correlation Analysis Between Computationally Light and Computationally Heavy Centrality Metrics. IGI Global. https://doi.org/10.4018/978-1-5225-3802-8.ch002

Chicago

Natarajan Meghanathan. "Computationally Light vs. Computationally Heavy Centrality Metrics: Correlation Analysis Between Computationally Light and Computationally Heavy Centrality Metrics." In Centrality Metrics for Complex Network Analysis: Emerging Research and Opportunities. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-3802-8.ch002

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Abstract

In this chapter, the authors analyze the correlation between the computationally light degree centrality (DEG) and local clustering coefficient complement-based degree centrality (LCC'DC) metrics vs. the computationally heavy betweenness centrality (BWC), eigenvector centrality (EVC), and closeness centrality (CLC) metrics. Likewise, they also analyze the correlation between the computationally light complement of neighborhood overlap (NOVER') and the computationally heavy edge betweenness centrality (EBWC) metric. The authors analyze the correlations at three different levels: pair-wise (Kendall's correlation measure), network-wide (Spearman's correlation measure), and linear regression-based prediction (Pearson's correlation measure). With regards to the node centrality metrics, they observe LCC'DC-BWC to be the most strongly correlated at all the three levels of correlation. For the edge centrality metrics, the authors observe EBWC-NOVER' to be strongly correlated with respect to the Spearman's correlation measure, but not with respect to the other two measures.

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