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 (R)/Octave code illustrating some of the algorithms will be available at: http://www.cambridge.org/9781107125773.

A hands-on, entry-level guide to algorithms for extracting information about social and economic behavior from network data.

About the Author
Francois Fouss received his PhD from the Universite catholique de Louvain, Belgium, where he is now Professor of Computer Science. His research and teaching interests include artificial intelligence, data mining, machine learning, pattern recognition, and natural language processing, with a focus on graph-based techniques. Marco Saerens received his PhD from the Universite Libre de Bruxelles, Belgium. He is now Professor of Computer Science at the Universite catholique de Louvain, Belgium. His research and teaching interests include artificial intelligence, data mining, machine learning, pattern recognition, and natural language processing, with a focus on graph-based techniques. Masashi Shimbo received his PhD from Kyoto University, Japan. He is now Associate Professor at the Graduate School of Information Science, Nara Institute of Science and Technology, Japan. His research and teaching interests include artificial intelligence, data mining, machine learning, pattern recognition, and natural language processing, with a focus on graph-based techniques.

Reviews
'This is a remarkable book that contains a coherent and unified presentation of many recent network data analysis concepts and algorithms. Rich with details and references, this is a book from which faculty and students alike will learn a lot!' Vincent Blondel, Universite Catholique de Louvain, Belgium
'An impressive compilation of motivation, derivations, and algorithms for a wealth of methods relevant to assessing distance and (dis)similarity, importance, labeling, and clustering of network nodes and links - tasks fundamental to network analysis in practice. The gathering of diverse elements from random walks, kernels, and other interrelated topics is particularly welcome.' Eric D. Kolaczyk, Boston University
'This is a reader-friendly up-to-date book covering all the major topics in static network data analysis. It both exposes the reader to the most advanced ideas in the field and provides the researcher with a toolbox of techniques to explore various structures: models involving the graph Laplacian, regularization methods, and Markov interpretations feature in this toolbox, among others.' Pavel Chebotarev, Institute of Control Sciences, Russian Academy of Sciences



Book Information
ISBN 9781107125773
Author Francois Fouss
Format Hardback
Page Count 543
Imprint Cambridge University Press
Publisher Cambridge University Press
Weight(grams) 1150g
Dimensions(mm) 261mm * 184mm * 33mm