Linking the Network Centrality Measures Closeness and Degree

TL;DR

This paper establishes a mathematical relationship between degree and closeness centrality, showing that inverse closeness depends linearly on the logarithm of degree, which simplifies understanding node importance in networks.

Contribution

It provides an explicit non-linear formula linking degree and closeness centrality, validated across various network models and real-world data, revealing redundancy in measuring closeness independently.

Findings

Inverse of closeness is linearly dependent on log of degree

Relationship holds across diverse network types

Closeness measurement may be redundant without degree correction

Abstract

Measuring the importance of nodes in a network with a centrality measure is a core task in any network application. There are many measures available and it is speculated that many encode similar information. We give an explicit non-linear relationship between two of the most popular measures of node centrality: degree and closeness. Based on a shortest-path tree approximation, we give an analytic derivation that shows the inverse of closeness is linearly dependent on the logarithm of degree. We show that our hypothesis works well for a range of networks produced from stochastic network models and for networks derived from 130 real-world data sets. We connect our results with previous results for other network distance scales such as average distance. Our results imply that measuring closeness is broadly redundant unless our relationship is used to remove the dependence on degree from closeness. The success of our relationship suggests that most networks can be approximated by shortest-path spanning trees which are all statistically similar two or more steps away from their root nodes.