Item description for Network Analysis: Methodological Foundations (Lecture Notes in Computer Science) by Ulrik Brandes & Thomas Erlebach...
?~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.
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Est. Packaging Dimensions: Length: 0.75" Width: 6.25" Height: 9.25" Weight: 1.8 lbs.
Release Date Mar 24, 2005
ISBN 3540249796 ISBN13 9783540249795
Availability 0 units.
More About Ulrik Brandes & Thomas Erlebach
Ulrik Brandes is professor of algorithmics in the Department of Computer and Information Science at the University of Konstanz.
Reviews - What do customers think about Network Analysis: Methodological Foundations (Lecture Notes in Computer Science)?
Great book! Mar 24, 2008
Generally, edited books are not good. But, this one is different as the editors did a good job in organizing the topic in such way that you will not realize that different chapters are written by different authors.
A great book for data mining students who are interested in social networks.