Learning Automata Approach for Social Networks
Language: English
Published by Springer International Publishing, 2019
Series: Book 325 of 538 - Studies in Computational Intelligence
- Hardcover
- New
Condition: New
£ 80.56
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Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Highlights recent advances in social network analysis Presents problems addressed by learning automata theory Includes topics concerning network centralities, models, problems, theories, algorithms, and their applicationsThis book b.
Seller Inventory # 257580643
- Title
- Learning Automata Approach for Social Networks
- Author
- Alireza Rezvanian|Behnaz Moradabadi|Mina Ghavipour|Mohammad Mehdi Daliri Khomami|Mohammad Reza Meybodi
- Publisher
- Springer International Publishing
- Publication year
- 2019
- Condition
- New
- Binding
- Gebunden
- Language
- English
- ISBN 10
- 3030107663
- ISBN 13
- 9783030107666
- Series
- Book 325 of 538: Studies in Computational Intelligence
- Seller catalogs
- Mathematik/Naturwissenschaften/Technik/Medizin
This book begins by briefly explaining learning automata (LA) models and a recently developed cellular learning automaton (CLA) named wavefront CLA. Analyzing social networks is increasingly important, so as to identify behavioral patterns in interactions among individuals and in the networks’ evolution, and to develop the algorithms required for meaningful analysis.
As an emerging artificial intelligence research area, learning automata (LA) has already had a significant impact in many areas of social networks. Here, the research areas related to learning and social networks are addressed from bibliometric and network analysis perspectives. In turn, the second part of the book highlights a range of LA-based applications addressing social network problems, from network sampling, community detection, link prediction, and trust management, to recommender systems and finally influence maximization. Given its scope, the book offers a valuable guide for all researchers whose work involves reinforcement learning, social networks and/or artificial intelligence."Synopsis" may belong to another edition of this title.
From the Back Cover
This book begins by briefly explaining learning automata (LA) models and a recently developed cellular learning automaton (CLA) named wavefront CLA. Analyzing social networks is increasingly important, so as to identify behavioral patterns in interactions among individuals and in the networks’ evolution, and to develop the algorithms required for meaningful analysis.
As an emerging artificial intelligence research area, learning automata (LA) has already had a significant impact in many areas of social networks. Here, the research areas related to learning and social networks are addressed from bibliometric and network analysis perspectives. In turn, the second part of the book highlights a range of LA-based applications addressing social network problems, from network sampling, community detection, link prediction, and trust management, to recommender systems and finally influence maximization. Given its scope, the book offers a valuable guide for all researchers whose work involves reinforcement learning, social networks and/or artificial intelligence."About the title" may belong to another edition of this title.
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