Temporal Scale of Processes in Dynamic Networks

Temporal streams of interactions are commonly aggregated into dynamic networks for temporal analysis. Results of this analysis are greatly affected by the resolution at which the original data are aggregated. The mismatch between the inherent temporal scale of the underlying process and that at which the analysis is performed can obscure important insights and lead to wrong conclusions. To this day, there is no established framework for choosing the appropriate scale for temporal analysis of streams of interactions. Our paper offers the first step towards the formalization of this problem. We show that for a general class of interaction streams it is possible to identify, in a principled way, the inherent temporal scale of the underlying dynamic processes. Moreover, we state important properties of these processes that can be used to develop an algorithm to identify this scale. Additionally, these properties can be used to separate interaction streams for which no level of aggregation is meaningful versus those that have a natural level of aggregation.