1.

Record Nr.

UNINA9910483456803321

Titolo

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

Pubbl/distr/stampa

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

Edizione

[1st ed. 2005.]

Descrizione fisica

1 online resource (XII, 472 p.)

Collana

Theoretical Computer Science and General Issues, , 2512-2029 ; ; 3418

Disciplina

004.2/1

Soggetti

Computer science—Mathematics

Discrete mathematics

Computer networks

Artificial intelligence—Data processing

Algorithms

Discrete Mathematics in Computer Science

Computer Communication Networks

Discrete Mathematics

Data Science

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Note generali

Bibliographic Level Mode of Issuance: Monograph

Nota di bibliografia

Includes bibliographical references (p. [439]-466) and index.

Nota di contenuto

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.

Sommario/riassunto

‘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.