Clean up or mess up: the effect of sampling biases on measurements of degree distributions in mobile phone datasets

Adeline Decuyper, Arnaud Browet, Vincent Traag, Vincent D. Blondel, Jean-Charles Delvenne

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.

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