A change of perspective in network centrality

Typing “Yesterday” into the search-bar of your browser provides a long list of websites with, in top places, a link to a video by The Beatles. The order your browser shows its search results is a notable example of the use of network centrality. Centrality measures the importance of the nodes in a network and it plays a crucial role in several felds, ranging from sociology to engineering, and from biology to economics. Many centrality metrics are available. However, these measures are generally based on ad hoc assumptions, and there is no commonly accepted way to compare the efectiveness and reliability of diferent metrics. Here we propose a new perspective where centrality defnition arises naturally from the most basic feature of a network, its adjacency matrix. Following this perspective, diferent centrality measures naturally emerge, including degree, eigenvector, and hub-authority centrality. Within this theoretical framework, the efectiveness of diferent metrics is evaluated and compared. Tests on a large set of networks show that the standard centrality metrics perform unsatisfactorily, highlighting intrinsic limitations for describing the centrality of nodes in complex networks. More informative multi-component centrality metrics are proposed as the natural extension of standard metrics.