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Directed contact networks (DCNs) are a particularly flexible and convenientclass of temporal networks, useful for modeling and analyzing the transfer ofdiscrete quantities in communications, transportation, epidemiology, etc.Transfers modeled by contacts typically underlie flows that associate multiplecontacts based on their spatiotemporal relationships. To infer these flows, weintroduce a simple inhomogeneous Markov model associated to a DCN and show howit can be effectively used for data reduction and anomaly detection through anexample of kernel-level information transfers within a computer.

A Markov model for inferring flows in directed contact networks