From Security to Community Detection in Social Networking Platforms

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Karampelas, Panagiotis (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Kawash, Jalal (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Özyer, Tansel (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2019.
• Έκδοση: 1st ed. 2019.
• Σειρά: Lecture Notes in Social Networks,
• Θέματα: Data mining.; Social sciences-Data processing.; Social sciences-Computer programs.; Big data.; Application software.; Statistical physics.; Dynamical systems.; Data Mining and Knowledge Discovery.; Computational Social Sciences.; Big Data/Analytics.; Computer Appl. in Social and Behavioral Sciences.; Complex Systems.

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...

Πίνακας περιεχομένων:

• 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.