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Learning automata approach for socia...

Rezvanian, Alireza.

Learning automata approach for social networks

Record Type:	Electronic resources : Monograph/item
Title/Author:	Learning automata approach for social networksby Alireza Rezvanian ... [et al.].
other author:	Rezvanian, Alireza.
Published:	Cham :Springer International Publishing :2019.
Description:	xvii, 329 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Online social networksData processing.
Online resource:	https://doi.org/10.1007/978-3-030-10767-3
ISBN:	9783030107673$q(electronic bk.)

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. 216..

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$q(electronic bk.)

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

775311
Online social networks
--Data processing.

LC Class. No.: HM742

Dewey Class. No.: 302.30285

Learning automata approach for social networks
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245 0 0 $a Learning automata approach for social networks $h [electronic resource] / $c by Alireza Rezvanian ... [et al.].
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a xvii, 329 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Studies in computational intelligence, $x 1860-949X ; $v v.820
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.
650 0 $a Online social networks $x Data processing. $3 775311
650 0 $a System analysis $x Data processing. $3 182728
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650 2 4 $a Artificial Intelligence. $3 212515
650 2 4 $a Social Media. $3 742054
700 1 $a Rezvanian, Alireza. $3 805572
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830 0 $a Studies in computational intelligence ; $v v. 216. $3 380871
856 4 0 $u https://doi.org/10.1007/978-3-030-10767-3
950 $a Intelligent Technologies and Robotics (Springer-42732)