Algorithms and Models for Network Data and Link Analysis
Cambridge University Press (Verlag)
978-1-107-12577-3 (ISBN)
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.
François Fouss received his PhD from the Université 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 Université Libre de Bruxelles, Belgium. He is now Professor of Computer Science at the Université 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.
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.
| Erscheinungsdatum | 07.07.2016 |
|---|---|
| Zusatzinfo | 7 Tables, black and white; 1 Halftones, black and white; 13 Line drawings, black and white |
| Verlagsort | Cambridge |
| Sprache | englisch |
| Maße | 184 x 261 mm |
| Gewicht | 1150 g |
| Themenwelt | Informatik ► Datenbanken ► Data Warehouse / Data Mining |
| Mathematik / Informatik ► Informatik ► Netzwerke | |
| Informatik ► Theorie / Studium ► Algorithmen | |
| ISBN-10 | 1-107-12577-4 / 1107125774 |
| ISBN-13 | 978-1-107-12577-3 / 9781107125773 |
| Zustand | Neuware |
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