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Learning Automata Approach for Social Networks

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Learning Automata Approach for Social Networks/ by Alireza Rezvanian, Behnaz Moradabadi, Mina Ghavipour, Mohammad Mehdi Daliri Khomami, Mohammad Reza Meybodi.
作者:	Rezvanian, Alireza.
其他作者:	Moradabadi, Behnaz.
面頁冊數:	XVII, 329 p. 107 illus., 72 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Computational intelligence. -
電子資源:	https://doi.org/10.1007/978-3-030-10767-3
ISBN:	9783030107673

Learning Automata Approach for Social Networks
Rezvanian, Alireza.

Learning Automata Approach for Social Networks[electronic resource] /by Alireza Rezvanian, Behnaz Moradabadi, Mina Ghavipour, Mohammad Mehdi Daliri Khomami, Mohammad Reza Meybodi. - 1st ed. 2019. - XVII, 329 p. 107 illus., 72 illus. in color.online resource. - Studies in Computational Intelligence,8201860-949X ;. - Studies in Computational Intelligence,564.

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:

568984
Computational intelligence.

LC Class. No.: Q342

Dewey Class. No.: 006.3

Learning Automata Approach for Social Networks
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