Centrality in complex networks under incomplete data

Abstract

The concept of centrality is one of the essential tools for analyzing complex systems. Over the years, a large number of centrality indices have been proposed that account for different aspects of a network. Unfortunately, most real networks are substantially incomplete, which affects the results of the centrality measures. This article aims to evaluate the sensitivity of 16 centrality measures to the presence of errors or incomplete information about the structure of a complex network. Our experiments are performed across 113 empirical networks. As a result, we identify centrality indices that are highly vulnerable to incomplete data.