Temporal Walk Centrality: Ranking Nodes in Evolving Networks

TL;DR

This paper introduces Temporal Walk Centrality, a novel measure for ranking nodes in evolving networks based on their ability to facilitate information flow through temporal random walks, capturing roles missed by traditional measures.

Contribution

It presents a new centrality measure for temporal networks, along with exact and approximation algorithms, and extends algebraic methods for counting walks in temporal settings.

Findings

Temporal walk centrality identifies key nodes missed by other measures.

Algorithms are efficient and accurate on real-world networks.

Rankings differ significantly from existing temporal centralities.

Abstract

We propose the Temporal Walk Centrality, which quantifies the importance of a node by measuring its ability to obtain and distribute information in a temporal network. In contrast to the widely-used betweenness centrality, we assume that information does not necessarily spread on shortest paths but on temporal random walks that satisfy the time constraints of the network. We show that temporal walk centrality can identify nodes playing central roles in dissemination processes that might not be detected by related betweenness concepts and other common static and temporal centrality measures. We propose exact and approximation algorithms with different running times depending on the properties of the temporal network and parameters of our new centrality measure. A technical contribution is a general approach to lift existing algebraic methods for counting walks in static networks to temporal networks. Our experiments on real-world temporal networks show the efficiency and accuracy of our algorithms. Finally, we demonstrate that the rankings by temporal walk centrality often differ significantly from those of other state-of-the-art temporal centralities.