© 2017

Prediction and Inference from Social Networks and Social Media

  • Jalal Kawash
  • Nitin Agarwal
  • Tansel Özyer


  • Demonstrates new mining techniques and applications for social networking within the fields of prediction and inference

  • Proposes a wide variety of social network research topics

  • Covers a wide variety of case studies and state-of-the-art analysis tools for Facebook and Twitter


Part of the Lecture Notes in Social Networks book series (LNSN)

Table of contents

  1. Front Matter
    Pages i-ix
  2. Mahnaz Roshanaei, Richard Han, Shivakant Mishra
    Pages 1-18
  3. Mouhamed Gaith Ayadi, Riadh Bouslimi, Jalel Akaichi, Hana Hedhli
    Pages 19-49
  4. Salim Afra, Alper Aksaç, Tansel Õzyer, Reda Alhajj
    Pages 97-114
  5. Baptiste de La Robertie, Yoann Pitarch, Olivier Teste
    Pages 115-140
  6. Christos Charitonidis, Awais Rashid, Paul J. Taylor
    Pages 141-170
  7. Tobias Hecking, Andreas Harrer, H. Ulrich Hoppe
    Pages 171-198
  8. Youssef Bouanan, Mathilde Forestier, Judicael Ribault, Gregory Zacharewicz, Bruno Vallespir
    Pages 199-225

About this book


This book addresses the challenges of social network and social media analysis in terms of prediction and inference. The chapters collected here tackle these issues by proposing new analysis methods and by examining mining methods for the vast amount of social content produced. Social Networks (SNs) have become an integral part of our lives; they are used for leisure, business, government, medical, educational purposes and have attracted billions of users. The challenges that stem from this wide adoption of SNs are vast. These include generating realistic social network topologies, awareness of user activities, topic and trend generation, estimation of user attributes from their social content, and behavior detection. This text has applications to widely used platforms such as Twitter and Facebook and appeals to students, researchers, and professionals in the field.


activity-based mood prediction overlapping community structures link prediction modeling multi-dimensional networks predicting collective action ranking social content trend prediction

Editors and affiliations

  • Jalal Kawash
    • 1
  • Nitin Agarwal
    • 2
  • Tansel Özyer
    • 3
  1. 1.Department of Computer ScienceUniversity of CalgaryCalgaryCanada
  2. 2.Information Science DepartmentUniversity of Arkansas at Little RockLittle RockUSA
  3. 3.Department of Computer EngineeringTOBB UniversityAnkaraTurkey

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