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Learning Automata Approach for Social Networks

  • Alireza Rezvanian
  • Behnaz Moradabadi
  • Mina Ghavipour
  • Mohammad Mehdi Daliri Khomami
  • Mohammad Reza Meybodi

Part of the Studies in Computational Intelligence book series (SCI, volume 820)

Table of contents

  1. Front Matter
    Pages i-xvii
  2. Alireza Rezvanian, Behnaz Moradabadi, Mina Ghavipour, Mohammad Mehdi Daliri Khomami, Mohammad Reza Meybodi
    Pages 1-49
  3. Alireza Rezvanian, Behnaz Moradabadi, Mina Ghavipour, Mohammad Mehdi Daliri Khomami, Mohammad Reza Meybodi
    Pages 51-74
  4. Alireza Rezvanian, Behnaz Moradabadi, Mina Ghavipour, Mohammad Mehdi Daliri Khomami, Mohammad Reza Meybodi
    Pages 75-89
  5. Alireza Rezvanian, Behnaz Moradabadi, Mina Ghavipour, Mohammad Mehdi Daliri Khomami, Mohammad Reza Meybodi
    Pages 91-149
  6. Alireza Rezvanian, Behnaz Moradabadi, Mina Ghavipour, Mohammad Mehdi Daliri Khomami, Mohammad Reza Meybodi
    Pages 151-168
  7. Alireza Rezvanian, Behnaz Moradabadi, Mina Ghavipour, Mohammad Mehdi Daliri Khomami, Mohammad Reza Meybodi
    Pages 169-239
  8. Alireza Rezvanian, Behnaz Moradabadi, Mina Ghavipour, Mohammad Mehdi Daliri Khomami, Mohammad Reza Meybodi
    Pages 241-279
  9. Alireza Rezvanian, Behnaz Moradabadi, Mina Ghavipour, Mohammad Mehdi Daliri Khomami, Mohammad Reza Meybodi
    Pages 281-313
  10. Alireza Rezvanian, Behnaz Moradabadi, Mina Ghavipour, Mohammad Mehdi Daliri Khomami, Mohammad Reza Meybodi
    Pages 315-329

About this book

Introduction

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.

Keywords

Social Networks Complex Social Networks Stochastic Graph Learning Automata Social Network Analysis Link Prediction Network Sampling Social Trust Trust Management Trust Network Collaborative Filtering Influence Maximization Community Detection

Authors and affiliations

  • Alireza Rezvanian
    • 1
  • Behnaz Moradabadi
    • 2
  • Mina Ghavipour
    • 3
  • Mohammad Mehdi Daliri Khomami
    • 4
  • Mohammad Reza Meybodi
    • 5
  1. 1.School of Computer ScienceInstitute for Research in Fundamental Sciences (IPM)TehranIran
  2. 2.Computer Engineering and Information Technology DepartmentAmirkabir University of Technology (Tehran Polytechnic)TehranIran
  3. 3.Computer Engineering and Information Technology DepartmentAmirkabir University of Technology (Tehran Polytechnic)TehranIran
  4. 4.Computer Engineering and Information Technology DepartmentAmirkabir University of Technology (Tehran Polytechnic)TehranIran
  5. 5.Computer Engineering and Information Technology DepartmentAmirkabir University of Technology (Tehran Polytechnic)TehranIran

Bibliographic information