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The authors present an analytically tractable mathematical approach for accurately modeling the distribution of inter-contact times between mobile devices carried by users. The contribution of this paper is two-fold: they show how to employ a MarkovModulated Poisson process (MMPP) for characterizing long-term dependencies in the mobility behavior, and they propose to employ a graph-based clustering approach for taking into account different user groups with inhomogeneous mobility patterns. They illustrate the effectiveness of the proposed approach by considering two comprehensive real-world trace data sets.
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