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The applicability of network centralities may depend on various factors. In practice, it is frequently challenging to understand which centralities are best applicable in a particular network. The current study addresses this challenge. It presents a method of ranking centralities that permits analysts to better understand which centralities are most appropriate for measuring positions in a particular network. The approach is based on the well-known F measure from the domain of statistical analysis of binary classification. Specifically, the classic F 1  estimator of classification accuracy of binary classifiers was adapted for estimating the accuracy of centralities to identify positions in networks.

Which Centralities Fit the Best? Network Centralities’ Ranking Based on the F-Measure