© 2017

Trends in Social Network Analysis

Information Propagation, User Behavior Modeling, Forecasting, and Vulnerability Assessment

  • Rokia Missaoui
  • Talel Abdessalem
  • Matthieu Latapy

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

Table of contents

  1. Front Matter
    Pages i-xiii
  2. David N. Fisher, Matthew J. Silk, Daniel W. Franks
    Pages 1-19
  3. Sirinya On-at, Arnaud Quirin, André Péninou, Nadine Baptiste-Jessel, Marie-Françoise Canut, Florence Sèdes
    Pages 21-50
  4. Santosh Kumar Bharti, Ramkrushna Pradhan, Korra Sathya Babu, Sanjay Kumar Jena
    Pages 51-76
  5. Roberto Interdonato, Chiara Pulice, Andrea Tagarelli
    Pages 77-106
  6. Byungkyu Kang, Haleigh Wright, Tobias Höllerer, Ambuj K. Singh, John O’Donovan
    Pages 135-168
  7. Jimpei Harada, David Darmon, Michelle Girvan, William Rand
    Pages 169-187
  8. Mehmet Kaya, Mujtaba Jawed, Ertan Bütün, Reda Alhajj
    Pages 189-205
  9. Ibrahima Gaye, Gervais Mendy, Samuel Ouya, Diaraf Seck
    Pages 207-228
  10. Eva Garcia-Martin, Niklas Lavesson, Håkan Grahn
    Pages 229-252
  11. Back Matter
    Pages 253-255

About this book


The book collects contributions from experts worldwide addressing recent scholarship in social network analysis such as influence spread, link prediction, dynamic network biclustering, and delurking. It covers both new topics and new solutions to known problems. The contributions rely on established methods and techniques in graph theory, machine learning, stochastic modelling, user behavior analysis and natural language processing, just to name a few. This text provides an understanding of using such methods and techniques in order to manage practical problems and situations. Trends in Social Network Analysis: Information Propagation, User Behavior Modelling, Forecasting, and Vulnerability Assessment appeals to students, researchers, and professionals working in the field.


Assortativity of Social networks energy efficient data mining social profile construction techniques sarcasm analysis in social networks social engineering vulnerability assessment Block Modelling in Dynamic Networks unsupervised link prediction method

Editors and affiliations

  • Rokia Missaoui
    • 1
  • Talel Abdessalem
    • 2
  • Matthieu Latapy
    • 3
  1. 1.Department of Computer Science & EngineeringUniversity of Quebec in OutaouaisGatineau, QCCanada
  2. 2.Department of Computer Science and NetworksTelecom ParisTechParisFrance
  3. 3.UPMC Univ Paris 06, CNRS LIP6 UMR 7606Sorbonne UniversitèsParisFrance

Bibliographic information

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“This volume is a selective post-proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Network Analysis and Mining. … The papers are uniformly of high quality and represent a good snapshot of the state of the art in the areas that they discuss. … The high quality of these carefully revised conference papers makes the volume of interest to researchers specializing in social networks who seek to stay abreast of recent developments.” (Computing Reviews, September, 2017)