Memory, Causality and Their Consequences in Temporal Networks

Abstract

Temporal networks are commonly used to represent systems where connections between elements are active only for restricted periods of time, such as in telecommunication systems, biochemical reactions or social networks. Such time-varying interactions may be biased by local correlations between causally related events and commonly influenced by memory effects, which in turn lead to the emergence of mesoscale temporal motifs, long time-respecting paths, and bursty interaction dynamics. In these talk we will address the consequences of memory driven and casually related interactions on the emerging network structure and on the outcome of ongoing dynamical processes.