Structural Analysis of Complex Networks

1. Front Matter

   Pages i-xiii

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2. A Brief Introduction to Complex Networks and Their Analysis

   • Frank Emmert-Streib

   Pages 1-26

3. Partitions of Graphs

   • Mieczysław Borowiecki

   Pages 27-47

4. Distance in Graphs

   • Wayne Goddard, Ortrud R. Oellermann

   Pages 49-72

5. Domination in Graphs

   • Nawarat Ananchuen, Watcharaphong Ananchuen, Michael D. Plummer

   Pages 73-104

6. Spectrum and Entropy for Infinite Directed Graphs

   • Jun Ichi Fujii

   Pages 105-136

7. Application of Infinite Labeled Graphs to Symbolic Dynamical Systems

   • Kengo Matsumoto

   Pages 137-168

8. Decompositions and Factorizations of Complete Graphs

   • Petr Kovář

   Pages 169-196

9. Geodetic Sets in Graphs

   • Boštjan Brešar, Matjaž Kovše, Aleksandra Tepeh

   Pages 197-218

10. Graph Polynomials and Their Applications I: The Tutte Polynomial

    • Joanna A. Ellis-Monaghan, Criel Merino

    Pages 219-255

11. Graph Polynomials and Their Applications II: Interrelations and Interpretations

    • Joanna A. Ellis-Monaghan, Criel Merino

    Pages 257-292

12. Reconstruction Problems for Graphs, Krawtchouk Polynomials, and Diophantine Equations

    • Thomas Stoll

    Pages 293-317

13. Subgraphs as a Measure of Similarity

    • Josef Lauri

    Pages 319-334

14. A Chromatic Metric on Graphs

    • Gerhard Benadé

    Pages 335-356

15. Some Applications of Eigenvalues of Graphs

    • Sebastian M. Cioabă

    Pages 357-379

16. Minimum Spanning Markovian Trees: Introducing Context-Sensitivity into the Generation of Spanning Trees

    • Alexander Mehler

    Pages 381-401

17. Link-Based Network Mining

    • Jerry Scripps, Ronald Nussbaum, Pang-Ning Tan, Abdol-Hossein Esfahanian

    Pages 403-419

18. Graph Representations and Algorithms in Computational Biology of RNA Secondary Structure

    • Stefan Washietl, Tanja GesellGesell

    Pages 421-437

19. Inference of Protein Function from the Structure of Interaction Networks

    • Oliver Mason, Mark Verwoerd, Peter Clifford

    Pages 439-461

20. Applications of Perfect Matchings in Chemistry

    • Damir Vukičević

    Pages 463-482

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: applications in biology, chemistry, linguistics, and data analysis; graph colorings; graph polynomials; information measures for graphs; metrical properties of graphs; partitions and decompositions; and 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
• combinatorics

From the reviews:

“The book Structural Analysis of Complex Networks presents theoretical as well as practice-oriented results for structurally exploring networks, combining graph-theoretic methods with mathematical techniques from other scientific disciplines such as machine learning, statistics and information theory. … the book is addressed to an interdisciplinary audience, covering topics from artificial intelligence, computer science, computational and systems biology, cognitive science, computational linguistics, discrete mathematics, machine learning, mathematical chemistry and statistics.” (Sanzaiana Caraman, IASI Polytechnic Magazine, Vol. 22 (1/4), March-December, 2010)

• Medizinische Informatik und Technik, Institute for Bioinformatics and Transla, UMIT-Private Universität für Gesundheits, Hall in Tirol, Austria

  Matthias Dehmer

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