Structural Analysis of Complex Networks 2011 edition

Marc Notes: Includes bibliographical references and index. 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 Kovar Geodetic Sets in Graphs \ Bo tjan Bre ar, Matjaz Kov e, 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 IndexJacket Description/Back: 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. Description for Sales People: Filling a gap in literature, this self-contained book presents theoretical and application-oriented results that allow 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. Applications to biology, chemistry, linguistics and data analysis are emphasized. The book 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. It may also be used as a supplementary textbook in graduate-level seminars on structural graph analysis, complex networks, or network-based machine learning methods. Review Quotes: 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) Publisher Marketing: This self-contained book presents theoretical and application-oriented results that allow readers to structurally explore complex networks.

Contributor Bio:  Dehmer, Matthias Frank Emmert-Streib studied physics at the University of Siegen, Germany, and received his PhD in Theoretical Physics from the University of Bremen, Germany. He is currently Senior Fellow at the University of Washington in Seattle, USA, in Biostatistics and Genome Sciences. Matthias Dehmer studied mathematics at the University of Siegen, Germany, and received his PhD in Computer Science from the Technical University of Darmstadt, Germany. Currently, he holds a research position at Vienna University of Technology, Institute of Discrete Mathematics and Geometry in Vienna, Austria.