  Mobile phone data have been extensively used in the recent years to study
social behavior. However, most of these studies are based on only partial data
whose coverage is limited both in space and time. In this paper, we point to an
observation that the bias due to the limited coverage in time may have an
important influence on the results of the analyses performed. In particular, we
observe significant differences, both qualitatively and quantitatively, in the
degree distribution of the network, depending on the way the dataset is
pre-processed and we present a possible explanation for the emergence of Double
Pareto LogNormal (DPLN) degree distributions in temporal data.
