Machine Learning Techniques for Online Social Networks

• Corporate Author: SpringerLink (Online service)
• Other Authors: Özyer, Tansel (Editor, http://id.loc.gov/vocabulary/relators/edt), Alhajj, Reda (Editor, http://id.loc.gov/vocabulary/relators/edt)
• Format: Electronic eBook
• Language: English
• Published: Cham : Springer International Publishing : Imprint: Springer, 2018.
• Edition: 1st ed. 2018.
• Series: Lecture Notes in Social Networks,
• Subjects: Social sciences-Data processing.; Social sciences-Computer programs.; Data mining.; Social media.; Artificial intelligence.; Computational Social Sciences.; Data Mining and Knowledge Discovery.; Social Media.; Artificial Intelligence.

The book covers tools in the study of online social networks such as machine learning techniques, clustering, and deep learning. A variety of theoretical aspects, application domains, and case studies for analyzing social network data are covered. The aim is to provide new perspectives on utilizing...

Table of Contents:

• Chapter1. Acceleration of Functional Cluster Extraction and Analysis of Cluster Affinity
• Chapter2. Delta-Hyperbolicity and the Core-Periphery Structure in Graphs
• Chapter3. A Framework for OSN Performance Evaluation Studies
• Chapter4. On The Problem of Multi-Staged Impression Allocation in Online Social Networks
• Chapter5. Order-of-Magnitude Popularity Estimation of Pirated Content
• Chapter6. Learning What to Share in Online Social Networks using Deep Reinforcement Learning
• Chapter7. Centrality and Community Scoring Functions in Incomplete Networks: Their Sensitivity, Robustness and Reliability
• Chapter8. Ameliorating Search Results Recommendation System based on K-means Clustering Algorithm and Distance Measurements
• Chapter9. Dynamics of large scale networks following a merger
• Chapter10. Cloud Assisted Personal Online Social Network
• Chapter11. Text-Based Analysis of Emotion by Considering Tweets.