Connectivity-based time centrality in time-varying graphs

Abstract Time-varying graphs (TVGs) enable the study and understanding of dynamics many real-world networked systems. The notion centrality in TVG scenarios generally refers to metrics that assess relative importance nodes over time evolution a complex dynamic network. In contrast, Time Centrality, which evaluates instants networks, is still little explored. Indeed, few works on Centrality base their findings diffusion processes, for example, identifying best instant start dissemination message envisaging more efficient given expected network dynamics. this work, we from connectivity perspective. context, propose two connectivity-based identify most important first metric Degree that, analogously node degree centrality, computes number connections each has. second metric, PageRank searches receive largest accumulated since they are considered weights at instant. To validate metrics, model public multimodal transport considering as an aspect node. We then apply proposed analyse results Our show can different