Time Scale of Dynamic Network

Interactions, either of molecules or people, are inherently dynamic, changing with time and context. Interactions have an inherent rhythm, often happening over a range of time scales. 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. In this talk I will describe the challenge of identifying the range of inherent temporal scales of a stream of interactions and of finding the dynamic network representation that matches those scales. I will describe possible formalizations of the problem of identifying the inherent time scales of interactions and present some initial approaches at solving it, noting the advantages and limitations of these approaches. This is a nascent area of research and the goal is to highlight its importance and to establish a computational foundation for further investigations.