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A Markov Model for Inferring Flows in Directed Contact Networks

  • Steve Huntsman
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 812)

Abstract

Directed contact networks (DCNs) are a particularly flexible and convenient class of temporal networks, useful for modeling and analyzing the transfer of discrete quantities in communications, transportation, etc. Transfers modeled by contacts typically underlie flows that associate multiple contacts based on their spatiotemporal relationships. To infer these flows, we introduce a simple inhomogeneous Markov model associated to a DCN and show how it can be effectively used for data reduction and anomaly detection through an example of kernel-level information transfers within a computer.

Keywords

Temporal networks Markov model 

Notes

Acknowledgements

The author thanks Yingbo Song, Rob Ross, and Mike Weber for many helpful discussions as well as creating the summary and ground truth data used in Sect. 4, and George Cybenko for still more helpful discussions. This material is based upon work supported by the Defense Advanced Research Projects Agency (DARPA) and the Air Force Research Laboratory (AFRL). Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of DARPA or AFRL.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.BAE Systems FAST LabsArlingtonUSA

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