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 machine learning and related scientific methods and techniques for social network analysis. Machine Learning Techniques for Online Social Networks will appeal to researchers and students in these fields.

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.