Alireza Rezvanian, Behnaz Moradabadi, Mina Ghavipour

Learning Automata Approach for Social Networks

• Gebundenes Buch

Produktbeschreibung

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.

Produktdetails

• Studies in Computational Intelligence 820
• Verlag: Springer / Springer International Publishing / Springer, Berlin
• Artikelnr. des Verlages: 978-3-030-10766-6
• 1st ed. 2019
• Seitenzahl: 348
• Erscheinungstermin: 31. Januar 2019
• Englisch
• Abmessung: 241mm x 160mm x 25mm
• Gewicht: 666g
• ISBN-13: 9783030107666
• ISBN-10: 3030107663
• Artikelnr.: 54630898

Inhaltsangabe

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.