A Binary Search Algorithm for Correlation Study of Decay Centrality vs. Degree Centrality and Closeness Centrality

Abstract

Results of correlation study (using Pearson's correlation coefficient, PCC) between decay centrality (DEC) vs. degree centrality (DEG) and closeness centrality (CLC) for a suite of 48 real-world networks indicate an interesting trend: PCC(DEC, DEG) decreases with increase in the decay parameter δ (0 < δ < 1) and PCC(DEC, CLC) decreases with decrease in δ . We make use of this trend of monotonic decrease in the PCC values (from both sides of the δ -search space) and propose a binary search algorithm that (given a threshold value r for the PCC) could be used to identify a value of δ (if one exists, we say there exists a positive δ - space r ) for a real-world network such that PCC(DEC, DEG) ≥ r as well as PCC(DEC, CLC) ≥ r . We show the use of the binary search algorithm to find the maximum Threshold PCC value r max (such that δ - space r max is positive) for a real-world network. We observe a very strong correlation between r max and PCC(DEG, CLC) as well as observe real-world networks with a larger variation in node degree to more likely have a lower r max value and vice-versa.