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Information Flows in Complex Networks

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Information Theory and Statistical Learning

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We give an overview on some of the main results in network information theory, that is the branch of Shannon theory that deals with the fundamental limits of information flow in complex networks. Particular emphasis is given to the fact that classical information-theoretic arguments, which yield the capacity of point-to-point channels, and standard network flow techniques, which are suitable for transport networks, do not necessarily apply when it comes to describing the behavior of information flows over complex networks that feature phenomena such as interference, cooperation or feedback. Notwithstanding this observation, we provide examples of information flow problems where max-flow min-cut type of arguments do prove useful for establishing performance bounds for complex networks and illustrate how mixing different flows through network coding may hold the key towards achieving those bounds.

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Author information

Authors and Affiliations

  1. Instituto de Telecomunicações, Universidade do Porto, Porto, Portugal

    João Barros

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  1. João Barros
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Correspondence to João Barros .

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Editors and Affiliations

  1. Department of Biostatistics and Department of Genome Sciences, University of Washington, 1705 NE Pacific St., Box 357730, Seattle, WA, 98195, USA

    Frank Emmert-Streib

  2. Queen's University Belfast Computational Biology and Machine Learning, Center for Cancer Research and Cell Biology School of Biomedical Sciences, 97 Lisburn Road, Belfast, BT9 7BL, UK

    Frank Emmert-Streib

  3. Institute of Discrete Mathematics and Geometry, Vienna University of Technology, Wiedner Hauptstr. 8–10, Vienna, 1040, Austria

    Matthias Dehmer

  4. Probability and Statistics, University of Coimbra Center for Mathematics, Apartado 3008, Coimbra, 3001–454, Portugal

    Matthias Dehmer

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Barros, J. (2009). Information Flows in Complex Networks. In: Emmert-Streib, F., Dehmer, M. (eds) Information Theory and Statistical Learning. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-84816-7_11

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  • DOI: https://doi.org/10.1007/978-0-387-84816-7_11

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-387-84815-0

  • Online ISBN: 978-0-387-84816-7

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