Applications of Temporal Graph Metrics to Real-World Networks

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.Comment: 25 page

Modern temporal network theory: a colloquium

“…Michalski et al [190] discuss an interesting way of omitting the time dependence by weighing older paths lighter. One can define temporal betweenness centrality [207] in a similar way [90,208,190,139,289,264,6]. Takaguchi et al [288] define "temporal coverage centrality" of i as the fraction of node pairs (j, j ) such that passing i would not increase the time to reach from j to j .…”

IoT-based cloud framework to control Ebola virus outbreak

“…Smith et al (2009) analyzed different kinds of social media networks for developing additional network metrics and analytical tools. Tang et al (2013) studied the use of TNA metrics to real-world networks. The authors demonstrated that metrics from temporal network analysis provide a more accurate information about dynamic contact networks.…”

Graph Metrics for Temporal Networks

“…As shown in Ref. [43] and elsewhere in this book [41], temporal closeness and betweenness centrality have proven useful to identify key spreaders and temporal mediators in corporate communication networks. In particular, it was found that traders indeed played an important mediatory role in time-varying graphs constructed from the ENRON email communication data set, being consistently ranked among the first ones both for temporal betweenness and for temporal closeness centrality.…”