Correlation analysis: edge betweenness centrality vs. neighbourhood overlap

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.