Correlation analysis: edge betweenness centrality vs. neighbourhood overlap. (11th September 2019)

• Record Type: Journal Article
• Authors: Meghanathan, Natarajan; Yang, Fei
• Is Part Of: International journal of network science. Volume 1:Number 4(2019)
• Page Start: 299
• Page End: 324
• Publication Date: 2019-09-11
• Subjects: edge betweenness centrality -- EBWC -- neighbourhood overlap -- NOVER -- correlation -- computationally-heavy -- computationally-light; Communication -- Network analysis -- Periodicals; Computer networks -- Mathematics -- Periodicals; Social networks -- Mathematics -- Periodicals; Semantic networks (Information theory) -- Periodicals; Biological systems -- Periodicals; 003.72
• Languages: English
• ISSNs: 2048-2175

Abstract

We explore the correlation between two well-known edge centrality metrics: a computationally-heavy metric called edge betweenness centrality (EBWC) and a computationally-light metric called neighbourhood overlap (NOVER). Three different correlation measures are used for the analysis: Spearman's correlation measure (for network-wide ranking), Kendall's correlation measure (for pair-wise relative ordering) and Pearson's correlation measure (for regression-based prediction). A suite of 47 real-world networks of diverse degree distributions have been used for the correlation study. Results of the correlation analysis indicate that NOVER could be used as a computationally-light alternative to rank the edges (network-wide) in lieu of EBWC, but might not be appropriate to predict the actual EBWC values.