Advertisement

Table of contents

  1. Front Matter
    Pages i-viii
  2. Takayasu Fushimi, Kazumi Saito, Tetsuo Ikeda, Kazuhiro Kazama
    Pages 1-22
  3. Pablo Nicolás Terevinto, Miguel Pérez, Josep Domenech, José A. Gil, Ana Pont
    Pages 45-64
  4. Charalampos Chelmis, Daphney-Stavroula Zois
    Pages 85-113
  5. Shatha Jaradat, Nima Dokoohaki, Mihhail Matskin, Elena Ferrari
    Pages 115-133
  6. Soumya Sarkar, Suhansanu Kumar, Sanjukta Bhowmick, Animesh Mukherjee
    Pages 135-154
  7. John Clements, Babak Farzad, Henryk Fukś
    Pages 173-193
  8. Esra Erdin, Eric Klukovich, Mehmet Hadi Gunes
    Pages 195-218
  9. Kashfia Sailunaz, Tansel Özyer, Jon Rokne, Reda Alhajj
    Pages 219-236

About this book

Introduction

The book covers tools in the study of online social networks such as machine learning techniques, clustering, and deep learning. A variety of theoretical aspects, application domains, and case studies for analyzing social network data are covered. The aim is to provide new perspectives on utilizing machine learning and related scientific methods and techniques for social network analysis. Machine Learning Techniques for Online Social Networks will appeal to researchers and students in these fields. 

Keywords

graph analysis online social network deep learning data analysis functional cluster extraction

Editors and affiliations

  • Tansel Özyer
    • 1
  • Reda Alhajj
    • 2
  1. 1.Department of Computer EngineeringTOBB University of Economics and TechnologyAnkaraTurkey
  2. 2.Department of Computer ScienceUniversity of CalgaryCalgaryCanada

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-89932-9
  • Copyright Information Springer International Publishing AG, part of Springer Nature 2018
  • Publisher Name Springer, Cham
  • eBook Packages Social Sciences
  • Print ISBN 978-3-319-89931-2
  • Online ISBN 978-3-319-89932-9
  • Series Print ISSN 2190-5428
  • Series Online ISSN 2190-5436
  • Buy this book on publisher's site