The analysis of human social dynamics stemming from the emergent effects of individual human interactions has recently created a spur of research activity. Encompassing a wide area ranging from the propagation of opinions,epidemic spreading of information and innovation across groups of individuals,
human dynamics investigates the temporal dependencies and interaction characteristics of the underlying contact and spreading processes, which are known to significantly drive overall diffusion and determine its contagion.
The analysis of the observed temporal distributions of online human activity data such as the inter-arrival and forwarding times of email, the propagation time of microblog posts,
or telephony holding times, have commonly been approached and modeled similar with the techniques established in topological network analysis, for example the approximation by power-law distributions.
The applicability of fitting temporal behavioral data by a power-law has however been questioned,and bears a number of complications. First, the approximation through a power-law primarily concentrates on the fat tail providing a sub-optimal fit for the lower part of the observations,
