Network Analysis : Methodological Foundations / edited by Ulrik Brandes, Thomas Erlebach.

Format:

Book

Contributor:

Brandes, Ulrik, editor.

Erlebach, Thomas, editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

LNCS sublibrary. Theoretical computer science and general issues ; SL 1, 3418.

Theoretical Computer Science and General Issues ; 3418

Language:

English

Subjects (All):

Computer science--Mathematics.

Computer science.

Computer networks.

Discrete mathematics.

Data structures (Computer science).

Algorithms.

Discrete Mathematics in Computer Science.

Computer Communication Networks.

Discrete Mathematics.

Data Structures.

Algorithm Analysis and Problem Complexity.

Local Subjects:

Discrete Mathematics in Computer Science.

Computer Communication Networks.

Discrete Mathematics.

Data Structures.

Algorithm Analysis and Problem Complexity.

Algorithms.

Physical Description:

1 online resource (XII, 472 pages).

Edition:

First edition 2005.

Contained In:

Springer eBooks

Place of Publication:

Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2005.

System Details:

text file PDF

Summary:

'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.

Contents:

Fundamentals

I Elements

Centrality Indices

Algorithms for Centrality Indices

Advanced Centrality Concepts

II Groups

Local Density

Connectivity

Clustering

Role Assignments

Blockmodels

Network Statistics

Network Comparison

Network Models

Spectral Analysis

Robustness and Resilience.

Other Format:

Printed edition:

ISBN:

978-3-540-31955-9

9783540319559

Access Restriction:

Restricted for use by site license.