Structural Analysis of Complex Networks

ISBN-13: 9780817647889 / Angielski / Twarda / 2010 / 486 str.

Matthias Dehmer

Opis

Filling a gap in literature, this self-contained book presents theoretical and application-oriented results that allow for a structural exploration of 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.

Recenzje

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)

Spis treści

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

O autorze

Matthias Dehmer studied mathematics at the University of Siegen (Germany) and received his PhD in computer science from the Technical University of Darmstadt (Germany). Afterwards, he was a research fellow at Vienna Bio Center (Austria), Vienna University of Technology and University of Coimbra (Portugal). Currently, he is Professor at UMIT - The Health and Life Sciences University (Austria). His research interests are in bioinformatics, cancer analysis, chemical graph theory, systems biology, complex networks, complexity, statistics and information theory. In particular, he is also working on machine learning-based methods to design new data analysis methods for solving problems in computational biology and medicinal chemistry.

Blurb

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.