From Security to Community Detection in Social Networking Platforms

From Security to Community Detection in Social Networking Platforms edited by Panagiotis Karampelas, Jalal Kawash, Tansel Özyer.

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 pr...

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Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: Karampelas, Panagiotis (Editor), Kawash, Jalal (Editor), Özyer, Tansel (Editor)
Format: eBook
Language:English
Published: Cham : Springer International Publishing : Imprint: Springer, 2019.
Edition:1st ed. 2019.
Series:Lecture Notes in Social Networks,
Springer eBook Collection.
Subjects:
Data mining.
Social sciences—Data processing.
Social sciences—Computer programs.
Big data.
Application software.
Statistical physics.
Dynamical systems.
Electronic resources (E-books)
Online Access:Click to view e-book
Holy Cross Note:Loaded electronically.
Electronic access restricted to members of the Holy Cross Community.
Table of Contents:
  • Chapter1. Real-world application of ego-network analysis to evaluate environmental management structures
  • Chapter2. An Evolutionary Approach for Detecting Communities in Social Networks
  • Chapter3. On Detecting Multidimensional Communities
  • Chapter4. Derivatives in Graph Space with Applications for Finding and Tracking Local Communities
  • Chapter5. Graph Clustering Based on Attribute-aware Graph Embedding
  • Chapter6. On Counting Triangles through Edge Sampling in Large Dynamic Graphs
  • Chapter7. Generation and Corruption of Semi-structured and Structured Data
  • Chapter8. A Data Science Approach to Predict the Impact of Collateralization on Systemic Risk
  • Chapter9. Mining actionable information from security forums: the case of malicious IP addresses
  • Chapter10. Temporal Methods to Detect Content-Based Anomalies in Social Media.

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