From Security to Community Detection in Social Networking Platforms

1. Auflage 2019
ISBN: 978-3-030-11285-1
Verlag: Springer International Publishing

Buch, Englisch, 237 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 541 g

Reihe: Lecture Notes in Social Networks

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.

Zielgruppe

Research

Autoren/Hrsg.

Karampelas, Panagiotis

O¿zyer, Tansel

Kawash, Jalal

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Big Data
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining
• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsinformatik, SAP, IT-Management
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Computeranwendungen in Geistes- und Sozialwissenschaften
• Naturwissenschaften Physik Angewandte Physik Statistische Physik, Dynamische Systeme
• Mathematik | Informatik EDV | Informatik Computerkommunikation & -vernetzung Social Media, Semantic Web, Web 2.0
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Wirtschaftsinformatik

Inhaltsverzeichnis

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.