Network Analysis Methodological Foundations /
Network Analysis Methodological Foundations / [electronic resource] :
edited by Ulrik Brandes, Thomas Erlebach.
- 1st ed. 2005.
- XII, 472 p. online resource.
- Theoretical Computer Science and General Issues, 3418 2512-2029 ; .
- Theoretical Computer Science and General Issues, 3418 .
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.
‘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.
9783540319559
10.1007/b106453 doi
Computer science--Mathematics.
Discrete mathematics.
Computer networks .
Artificial intelligence--Data processing.
Algorithms.
Discrete Mathematics in Computer Science.
Computer Communication Networks.
Discrete Mathematics.
Data Science.
Algorithms.
QA76.9.M35 QA297.4
004.0151
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.
‘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.
9783540319559
10.1007/b106453 doi
Computer science--Mathematics.
Discrete mathematics.
Computer networks .
Artificial intelligence--Data processing.
Algorithms.
Discrete Mathematics in Computer Science.
Computer Communication Networks.
Discrete Mathematics.
Data Science.
Algorithms.
QA76.9.M35 QA297.4
004.0151