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  • © 2019

From Security to Community Detection in Social Networking Platforms

Editors:

(view affiliations)
  • Contains state-of-the-art methodologies for community detection

  • Features prediction techniques based on social network analysis

  • Includes detailed tables, illustrative figures, and techniques for graph analysis

Conference proceedings info: ASONAM 2017.

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  • ISBN: 978-3-030-11286-8
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Table of contents (10 chapters)

  1. Front Matter

    Pages i-x
  2. Real-World Application of Ego-Network Analysis to Evaluate Environmental Management Structures

    • Andreea Nita, Steluta Manolache, Cristiana M. Ciocanea, Laurentiu Rozylowicz
    Pages 1-16
  3. An Evolutionary Approach for Detecting Communities in Social Networks

    • Koray Ozturk, Faruk Polat, Tansel Özyer
    Pages 17-44
  4. On Detecting Multidimensional Communities

    • Amani Chouchane, Oualid Boutemine, Mohamed Bouguessa
    Pages 45-78
  5. Derivatives in Graph Space with Applications for Finding and Tracking Local Communities

    • M. Amin Rigi, Irene Moser, M. Mehdi Farhangi
    Pages 79-107
  6. Graph Clustering Based on Attribute-Aware Graph Embedding

    • Esra Akbas, Peixiang Zhao
    Pages 109-131
  7. Generation and Corruption of Semi-Structured and Structured Data

    • Samir Al-janabi, Ryszard Janicki
    Pages 159-169
  8. A Data Science Approach to Predict the Impact of Collateralization on Systemic Risk

    • Sharyn O’Halloran, Nikolai Nowaczyk, Donal Gallagher, Vivek Subramaniam
    Pages 171-192
  9. Mining Actionable Information from Security Forums: The Case of Malicious IP Addresses

    • Joobin Gharibshah, Tai Ching Li, Andre Castro, Konstantinos Pelechrinis, Evangelos E. Papalexakis, Michalis Faloutsos
    Pages 193-211
  10. Temporal Methods to Detect Content-Based Anomalies in Social Media

    • Jacek Skryzalin, Richard Field Jr., Andrew Fisher, Travis Bauer
    Pages 213-230
  11. Back Matter

    Pages 231-237

About this book

This book focuses on novel and state-of-the-art scientific work in the area of detection and prediction techniques using information found generally in graphs and particularly in social networks. Community detection techniques are presented in diverse contexts and for different applications while prediction methods for structured and unstructured data are applied to a variety of fields such as financial systems, security forums, and social networks. The rest of the book focuses on graph-based techniques for data analysis such as graph clustering and edge sampling. 

The research presented in this volume was selected based on solid reviews from the IEEE/ACM International Conference on Advances in Social Networks, Analysis, and Mining (ASONAM '17). Chapters were then improved and extended substantially, and the final versions were rigorously reviewed and revised to meet the series standards. This book will appeal to practitioners, researchers and students in the field.

Keywords

  • big data and social networks
  • influence analysis
  • pattern recognition
  • recommendation systems
  • prediction analysis
  • big data streams
  • sentiment analysis
  • community detection
  • environmental management structures
  • mining actionable information
  • computational social sciences

Editors and Affiliations

  • Department of Informatics & Computers, Hellenic Air Force Academy, Dekelia, Greece

    Panagiotis Karampelas

  • Department of Computer Science, University of Calgary, Calgary, Canada

    Jalal Kawash

  • Department of Computer Engineering, TOBB University of Economics and Technology, Ankara, Turkey

    Tansel Özyer

Bibliographic Information

Buying options

eBook
USD 129.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-11286-8
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Hardcover Book
USD 169.99
Price excludes VAT (USA)