Structural Analysis of Complex Networks

  • Matthias Dehmer

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Mieczysław Borowiecki
    Pages 27-47
  3. Wayne Goddard, Ortrud R. Oellermann
    Pages 49-72
  4. Nawarat Ananchuen, Watcharaphong Ananchuen, Michael D. Plummer
    Pages 73-104
  5. Boštjan Brešar, Matjaž Kovše, Aleksandra Tepeh
    Pages 197-218
  6. Joanna A. Ellis-Monaghan, Criel Merino
    Pages 219-255
  7. Joanna A. Ellis-Monaghan, Criel Merino
    Pages 257-292
  8. Josef Lauri
    Pages 319-334
  9. Gerhard Benadé
    Pages 335-356
  10. Sebastian M. Cioabă
    Pages 357-379
  11. Jerry Scripps, Ronald Nussbaum, Pang-Ning Tan, Abdol-Hossein Esfahanian
    Pages 403-419
  12. Oliver Mason, Mark Verwoerd, Peter Clifford
    Pages 439-461
  13. Damir Vukičević
    Pages 463-482
  14. Back Matter
    Pages 483-486

About this book


Because of the increasing complexity and growth of real-world networks, their analysis by using classical graph-theoretic methods is oftentimes a difficult procedure. As a result, there is a strong need to combine graph-theoretic methods with mathematical techniques from other scientific disciplines, such as machine learning and information theory, in order to analyze complex networks more adequately.

Filling a gap in literature, this self-contained book presents theoretical and application-oriented results to structurally explore complex networks. The work focuses not only on classical graph-theoretic methods, but also demonstrates the usefulness of structural graph theory as a tool for solving interdisciplinary problems.

Special emphasis is given to methods related to the following areas:

* Applications to biology, chemistry, linguistics, and data analysis

* Graph colorings

* Graph polynomials

* Information measures for graphs

* Metrical properties of graphs

* Partitions and decompositions

* Quantitative graph measures

Structural Analysis of Complex Networks is suitable for a broad, interdisciplinary readership of researchers, practitioners, and graduate students in discrete mathematics, statistics, computer science, machine learning, artificial intelligence, computational and systems biology, cognitive science, computational linguistics, and mathematical chemistry. The book may be used as a supplementary textbook in graduate-level seminars on structural graph analysis, complex networks, or network-based machine learning methods.


artificial intelligence biological networks complex networks computational and systems biology computational linguistics data mining graph polynomials graph representations mathematical chemistry network-based machine learning methods planar graphs structural graph analysis structural network analysis subgraphs

Editors and affiliations

  • Matthias Dehmer
    • 1
  1. 1.Medizinische Informatik und Technik, Institute for Bioinformatics and TranslaUMIT-Private Universität für GesundheitsHall in TirolAustria

Bibliographic information