Algorithms and models for network data and link analysis

By: Fouss, Francois
Contributor(s): Saerens, Marco | Shimbo, Masashi
Material type: TextTextPublisher: Cambridge Cambridge University Press 2016Description: xv, 521 p.ISBN: 9781107125773Subject(s): Network analysis | Planning | MathematicsDDC classification: 004.65 Summary: 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. Unifies algorithms from diverse fields, including applied mathematics, computer science and physics The task-based approach focuses on what information needs to be extracted, then on how to do it Derives algorithms in detail and summarizes in pseudo-code to support implementation and adaptation. http://admin.cambridge.org/hu/academic/subjects/computer-science/knowledge-management-databases-and-data-mining/algorithms-and-models-network-data-and-link-analysis?format=HB
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Table of Contents

1. Preliminaries and notation
2. Similarity/proximity measures between nodes
3. Families of dissimilarity between nodes
4. Centrality measures on nodes and edges
5. Identifying prestigious nodes
6. Labeling nodes: within-network classification
7. Clustering nodes
8. Finding dense regions
9. Bipartite graph analysis
10. Graph embedding.

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.

Unifies algorithms from diverse fields, including applied mathematics, computer science and physics
The task-based approach focuses on what information needs to be extracted, then on how to do it
Derives algorithms in detail and summarizes in pseudo-code to support implementation and adaptation.

http://admin.cambridge.org/hu/academic/subjects/computer-science/knowledge-management-databases-and-data-mining/algorithms-and-models-network-data-and-link-analysis?format=HB

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