Applications of Temporal Graph Metrics to Real-World Networks

TL;DR

This paper explores how temporal graph metrics can be applied to analyze real-world networks more accurately than static methods, demonstrating their usefulness in various practical scenarios such as corporate, social, and biological networks.

Contribution

It introduces the application of temporal centrality and efficiency metrics to real-world networks, showcasing their advantages over static analysis in multiple contexts.

Findings

Temporal metrics identify key individuals in corporate networks.

Temporal analysis improves robustness assessment of networks.

Effective strategies for virus containment and immunization are developed.

Abstract

Real world networks exhibit rich temporal information: friends are added and removed over time in online social networks; the seasons dictate the predator-prey relationship in food webs; and the propagation of a virus depends on the network of human contacts throughout the day. Recent studies have demonstrated that static network analysis is perhaps unsuitable in the study of real world network since static paths ignore time order, which, in turn, results in static shortest paths overestimating available links and underestimating their true corresponding lengths. Temporal extensions to centrality and efficiency metrics based on temporal shortest paths have also been proposed. Firstly, we analyse the roles of key individuals of a corporate network ranked according to temporal centrality within the context of a bankruptcy scandal; secondly, we present how such temporal metrics can be used to study the robustness of temporal networks in presence of random errors and intelligent attacks; thirdly, we study containment schemes for mobile phone malware which can spread via short range radio, similar to biological viruses; finally, we study how the temporal network structure of human interactions can be exploited to effectively immunise human populations. Through these applications we demonstrate that temporal metrics provide a more accurate and effective analysis of real-world networks compared to their static counterparts.