Algorithms and Models for Network Data and Link Analysis

Nonfiction, Computers, Advanced Computing, Computer Science, Database Management, General Computing
Cover of the book Algorithms and Models for Network Data and Link Analysis by François Fouss, Marco Saerens, Masashi Shimbo, Cambridge University Press
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Author: François Fouss, Marco Saerens, Masashi Shimbo ISBN: 9781316711941
Publisher: Cambridge University Press Publication: July 12, 2016
Imprint: Cambridge University Press Language: English
Author: François Fouss, Marco Saerens, Masashi Shimbo
ISBN: 9781316711941
Publisher: Cambridge University Press
Publication: July 12, 2016
Imprint: Cambridge University Press
Language: English

Network data are produced automatically by everyday interactions - social networks, power grids, and links between data sets are a few examples. Such data capture social and economic behavior in a form that can be analyzed using powerful computational tools. This book is a guide to both basic and advanced techniques and algorithms for extracting useful information from network data. The content is organized around 'tasks', grouping the algorithms needed to gather specific types of information and thus answer specific types of questions. Examples include similarity between nodes in a network, prestige or centrality of individual nodes, and dense regions or communities in a network. Algorithms are derived in detail and summarized in pseudo-code. The book is intended primarily for computer scientists, engineers, statisticians and physicists, but it is also accessible to network scientists based in the social sciences. MATLAB®/Octave code illustrating some of the algorithms will be available at: http://www.cambridge.org/9781107125773.

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Network data are produced automatically by everyday interactions - social networks, power grids, and links between data sets are a few examples. Such data capture social and economic behavior in a form that can be analyzed using powerful computational tools. This book is a guide to both basic and advanced techniques and algorithms for extracting useful information from network data. The content is organized around 'tasks', grouping the algorithms needed to gather specific types of information and thus answer specific types of questions. Examples include similarity between nodes in a network, prestige or centrality of individual nodes, and dense regions or communities in a network. Algorithms are derived in detail and summarized in pseudo-code. The book is intended primarily for computer scientists, engineers, statisticians and physicists, but it is also accessible to network scientists based in the social sciences. MATLAB®/Octave code illustrating some of the algorithms will be available at: http://www.cambridge.org/9781107125773.

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