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Network Analysis

Methodological Foundations

  • Textbook
  • © 2005

Overview

Editors:
  1. Ulrik Brandes

    1. Department of Computer & Information Science, University of Konstanz,  

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    You can also search for this editor in PubMed   Google Scholar

  2. Thomas Erlebach

    1. Department of Computer Science, University of Leicester, Leicester, UK

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    You can also search for this editor in PubMed   Google Scholar

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 3418)

Part of the book sub series: Theoretical Computer Science and General Issues (LNTCS)

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Table of contents (15 chapters)

  1. Front Matter

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  2. Introduction

    1. Introduction

      • Ulrik Brandes, Thomas Erlebach
      Pages 1-6

    2. Fundamentals

      • Ulrik Brandes, Thomas Erlebach
      Pages 7-15

  3. Part I Elements

    1. Centrality Indices

      • Dirk Koschützki, Katharina Anna Lehmann, Leon Peeters, Stefan Richter, Dagmar Tenfelde-Podehl, Oliver Zlotowski
      Pages 16-61

    2. Algorithms for Centrality Indices

      • Riko Jacob, Dirk Koschützki, Katharina Anna Lehmann, Leon Peeters, Dagmar Tenfelde-Podehl
      Pages 62-82

    3. Advanced Centrality Concepts

      • Dirk Koschützki, Katharina Anna Lehmann, Dagmar Tenfelde-Podehl, Oliver Zlotowski
      Pages 83-111

  4. Part II Groups

    1. Local Density

      • Sven Kosub
      Pages 112-142

    2. Connectivity

      • Frank Kammer, Hanjo Täubig
      Pages 143-177

    3. Clustering

      • Marco Gaertler
      Pages 178-215

    4. Role Assignments

      • Jürgen Lerner
      Pages 216-252

    5. Blockmodels

      • Marc Nunkesser, Daniel Sawitzki
      Pages 253-292

    6. Network Statistics

      • Michael Brinkmeier, Thomas Schank
      Pages 293-317

    7. Network Comparison

      • Michael Baur, Marc Benkert
      Pages 318-340

    8. Network Models

      • Nadine Baumann, Sebastian Stiller
      Pages 341-372

    9. Spectral Analysis

      • Andreas Baltz, Lasse Kliemann
      Pages 373-416

    10. Robustness and Resilience

      • Gunnar W. Klau, René Weiskircher
      Pages 417-437

  5. Back Matter

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Keywords

About this book

‘Network’ is a heavily overloaded term, so that ‘network analysis’ means different things to different people. Specific forms of network analysis are used in the study of diverse structures such as the Internet, interlocking directorates, transportation systems, epidemic spreading, metabolic pathways, the Web graph, electrical circuits, project plans, and so on. There is, however, a broad methodological foundation which is quickly becoming a prerequisite for researchers and practitioners working with network models.

From a computer science perspective, network analysis is applied graph theory. Unlike standard graph theory books, the content of this book is organized according to methods for specific levels of analysis (element, group, network) rather than abstract concepts like paths, matchings, or spanning subgraphs. Its topics therefore range from vertex centrality to graph clustering and the evolution of scale-free networks.

In 15 coherent chapters, this monograph-like tutorial book introduces and surveys the concepts and methods that drive network analysis, and is thus the first book to do so from a methodological perspective independent of specific application areas.

Editors and Affiliations

  • Department of Computer & Information Science, University of Konstanz,  

    Ulrik Brandes

  • Department of Computer Science, University of Leicester, Leicester, UK

    Thomas Erlebach

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