9. Information-Based Complexity of Networks

  1. Matthias Dehmer2,
  2. Abbe Mowshowitz3 and
  3. Frank Emmert-Streib3
  1. Russell K. Standish

Published Online: 12 JUL 2013

DOI: 10.1002/9783527670468.ch09

Advances in Network Complexity

Advances in Network Complexity

How to Cite

Standish, R. K. (2013) Information-Based Complexity of Networks, in Advances in Network Complexity (eds M. Dehmer, A. Mowshowitz and F. Emmert-Streib), Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim, Germany. doi: 10.1002/9783527670468.ch09

Editor Information

  1. 2

    UMIT, Institut für Bioinformatik und, Translationale Forschung, Eduard-Wallnöfer-Zentrum 1, 6060 Hall in Tyrol, Austria

  2. 3

    The City College of New York, Department of Computer Science, 138th Street at Convent Avenue, New York, NY 10031, USA

Author Information

  1. University of New South Wales, Mathematics and Statistics, Sydney, NSW, 2052, Australia

Publication History

  1. Published Online: 12 JUL 2013
  2. Published Print: 10 JUL 2013

ISBN Information

Print ISBN: 9783527332915

Online ISBN: 9783527670468



  • Boolean networks;
  • dynamical processes;
  • Gaussian processes;
  • information-based measures;
  • network complexity


This chapter presents a number of information-based measures of network complexity. Measures of structural complexity are found to be related to each other, and similarly information flow measures of dynamic complexity are also found to be related. It would seem plausible that dynamic complexity measures should be related to structural complexity when the dynamical processes are in some sense generic, or uncolored, but at this stage, such a conjecture remains unproven. For relatively simple processes such as Gaussian processes, and the random Boolean networks studied by Lizier et al., the behavior of a dynamical complexity measure has a peak at much lower connectivities than the peak exhibited by the structural complexity measure.