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More About This Textbook
Overview
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.
Editorial Reviews
From the Publisher
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)
Product Details
Table of Contents
Preface
A Brief Introduction to Complex Networks and Their Analysis \ Frank Emmert-Streib
Partitions of Graphs \ Mieczyslaw Borowiecki
Distance in Graphs \ Wayne Goddard and Ortrud Oellerman
Domination in Graphs \ Nawarat Ananchuen
Spectrum and Entropy for Infinite Directed Graphs \ Jun Ichi Fujij
Application of Infinite Labeled Graphs to Symbolic Dynamical Systems \ Kengo Matsumoto
Decompositions and Factorizations of Complete Graphs \ Petr Kovár
Geodetic Sets in Graphs \ Boštjan Brešar, Matjaz Kovše, and Aleksandra Tepeh Horvat
Graph Polynomials and Their Applications I: The Tutte Polynomial \ Joanna Ellis-Monaghan and Criel Merino
Graph Polynomials and Their Applications II: Interrelations and Interpretations \ Joanna Ellis-Monaghan and Criel Merino
Reconstruction Problems for Graphs, Krawtchouk Polynomials and Diophantine Equations \ Thomas Stoll
Subgraphs as a Measure of Similarity \ Josef Lauri
A Chromatic Metric on Graphs \ Gerhard Benade
Some Applications of Eigenvalues of Graphs \ Sebastian Cioaba
Minimum Spanning Markovian Trees: Introducing Context-Sensitivity into the Generation of Spanning Trees \ Alexander Mehler
Link-Based Network Mining \ Jerry Scripps, Ronald Nussbaum, Pang-Ning Tan, and Abdol-Hossein Esfahanian
Graph Representations and Algorithms in Computational Biology of RNA Secondary Structure \ Stefan Washietl and Tanja Gesell
Inference of Protein Function from the Structure of Interaction Networks \ Oliver Mason, Peter Clifford, and Mark Verwoerd
Applications of Perfect Matchings in Chemistry \ Damir Vukicevic
Index