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Informationen zum Autor 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. Klappentext A hands-on, entry-level guide to algorithms for extracting information about social and economic behavior from network data. Zusammenfassung Network data capture social and economic behavior in a form that can be analyzed using computational tools. In this entry-level guide! algorithms for extracting information are derived in detail and summarized in pseudo-code. This book is intended primarily for computer scientists! engineers! statisticians! and physicists! but it is also accessible to social network scientists more broadly. Inhaltsverzeichnis 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....
