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Learning automata approach for socia...

Rezvanian, Alireza.

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Learning automata approach for social networks

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Learning automata approach for social networks/ by Alireza Rezvanian ... [et al.].
其他作者:	Rezvanian, Alireza.
出版者:	Cham :Springer International Publishing : : 2019.,
面頁冊數:	xvii, 329 p. :ill. (some col.), digital ;24 cm.
內容註:	Introduction to Learning Automata Models -- Wavefront Cellular Learning Automata: A New Learning Paradigm -- Social Networks and Learning Systems: A Bibliometric Analysis -- Social Network Sampling -- Social Community Detection -- Social Link Prediction -- Social Trust Management -- Social Recommender Systems -- Social Influence Maximization.
Contained By:	Springer eBooks
標題:	Online social networks - Data processing. -
電子資源:	https://doi.org/10.1007/978-3-030-10767-3
ISBN:	9783030107673

Learning automata approach for social networks
Learning automata approach for social networks[electronic resource] /by Alireza Rezvanian ... [et al.]. - Cham :Springer International Publishing :2019. - xvii, 329 p. :ill. (some col.), digital ;24 cm. - Studies in computational intelligence,v.8201860-949X ;. - Studies in computational intelligence ;v.820..

Introduction to Learning Automata Models -- Wavefront Cellular Learning Automata: A New Learning Paradigm -- Social Networks and Learning Systems: A Bibliometric Analysis -- Social Network Sampling -- Social Community Detection -- Social Link Prediction -- Social Trust Management -- Social Recommender Systems -- Social Influence Maximization.

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.

ISBN: 9783030107673

Standard No.: 10.1007/978-3-030-10767-3doiSubjects--Topical Terms:

3226542
Online social networks
--Data processing.

LC Class. No.: HM742

Dewey Class. No.: 302.30285

Learning automata approach for social networks
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505 0 $a Introduction to Learning Automata Models -- Wavefront Cellular Learning Automata: A New Learning Paradigm -- Social Networks and Learning Systems: A Bibliometric Analysis -- Social Network Sampling -- Social Community Detection -- Social Link Prediction -- Social Trust Management -- Social Recommender Systems -- Social Influence Maximization.
520 $a 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.
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