Towards an Information Theory of Complex Networks

Statistical Methods and Applications

  • Matthias Dehmer
  • Frank Emmert-Streib
  • Alexander Mehler

Table of contents

  1. Front Matter
    Pages i-xvi
  2. Nicolas Bonichon, Cyril Gavoille, Nicolas Hanusse
    Pages 17-46
  3. Richard Berkovits, Lukas Jahnke, Jan W. Kantelhardt
    Pages 75-96
  4. Prabhat K. Sahu, Shyi-Long Lee
    Pages 127-151
  5. Robert E. Ulanowicz
    Pages 153-167
  6. C. R. Munteanu, J. Dorado, Alejandro Pazos-Sierra, F. Prado-Prado, L. G. Pérez-Montoto, S. Vilar et al.
    Pages 199-258
  7. Edward B. Allen
    Pages 347-364
  8. Philippe Blanchard, Dimitri Volchenkov
    Pages 365-395

About this book


For over a decade, complex networks have steadily grown as an important tool across a broad array of academic disciplines, with applications ranging from physics to social media. A  tightly organized collection of carefully-selected papers on the subject, Towards an Information Theory of Complex Networks: Statistical Methods and Applications presents theoretical and practical results about information-theoretic and statistical models of complex networks in the natural sciences and humanities. The book's major goal is to advocate and promote a combination of graph-theoretic, information-theoretic, and statistical methods as a way to better understand and characterize real-world networks.

This volume is the first to present a self-contained, comprehensive overview of information-theoretic models of complex networks with an emphasis on applications. It begins with four chapters developing the most significant formal-theoretical issues of network modeling, but the majority of the book is devoted to combining theoretical results with an empirical analysis of real networks. Specific topics include:

  • chemical graph theory
  • ecosystem interaction dynamics
  • social ontologies
  • language networks
  • software systems

This work marks a first step toward establishing advanced statistical information theory as a unified theoretical basis of complex networks for all scientific disciplines. As such, it can serve as a valuable resource for a diverse audience of advanced students and professional scientists. It is primarily intended as a reference for research, but could also be a useful supplemental graduate text in courses related to information science, graph theory, machine learning, and computational biology, among others.


complexity data analysis entropy information theory networks

Editors and affiliations

  • Matthias Dehmer
    • 1
  • Frank Emmert-Streib
    • 2
  • Alexander Mehler
    • 3
  1. 1.Medizinische Informatik und Technik, Institute for Bioinformatics and TranslaUMIT-Private Universität für GesundheitsHall in TirolAustria
  2. 2.Queen's University Belfast, School of Medicine, Dentistry, and CellCenter for Cancer Research & Cell BiologBelfastUnited Kingdom
  3. 3.Goethe-University Frankfurt am Main, Department of Philosophy and HistoricalCenter for Computing in the HumanitiesFrankfurtGermany

Bibliographic information

Industry Sectors