Image from Google Jackets

Learning automata approach for social networks / by Alireza Rezvanian, Behnaz Moradabadi, Mina Ghavipour, Mohammad Mehdi Daliri Khomami, Mohammad Reza Meybodi

By: Rezvanian, Alireza, autor
Contributor(s): SpringerLink (Online service) | Moradabadi, Behnaz, autor | Ghavipour, Mina, autor | Daliri Khomami, Mohammad Mehdi, autor | Meybodi, Mohammad Reza., autor
Series: (Studies in Computational Intelligence, 1860-949X; 820); (Intelligent Technologies and Robotics (Springer-42732)).Publisher: Cham : Springer International Publishing : Imprint: Springer, 2019Description: 1 recurso en línea (XVII, 329 páginas) : 107 ilustraciones, 72 ilustraciones a color.ISBN: 9783030107673.Subject: Aprendizaje automático | Inteligencia artificial | Redes socialesOnline resources: Acceso a este recurso digital (usuarios Universidad Europea de Madrid)Digital Resources
Contents:
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.
Abstract: 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.
Tags from this library: No tags from this library for this title. Log in to add tags.
Star ratings
    Average rating: 0.0 (0 votes)
Holdings
Item type Current library Collection Call number Status Date due Barcode Item holds
LIBRO-E NO PRÉSTAMO LIBRO-E NO PRÉSTAMO Madrid Digital Acceso Electrónico (UEM) Ciencias e Ingeniería Q325 2019 EB (Browse shelf(Opens below)) Acceso electrónico eBooks26062157
Total holds: 0

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.

There are no comments on this title.

to post a comment.