Machine Learning Techniques for Online Social Networks

1. Front Matter

   Pages i-viii

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2. Acceleration of Functional Cluster Extraction and Analysis of Cluster Affinity

   • Takayasu Fushimi, Kazumi Saito, Tetsuo Ikeda, Kazuhiro Kazama

   Pages 1-22

3. δ-Hyperbolicity and the Core-Periphery Structure in Graphs

   • Hend Alrasheed

   Pages 23-43

4. A Framework for OSN Performance Evaluation Studies

   • Pablo Nicolás Terevinto, Miguel Pérez, Josep Domenech, José A. Gil, Ana Pont

   Pages 45-64

5. On the Problem of Multi-Staged Impression Allocation in Online Social Networks

   • Inzamam Rahaman, Patrick Hosein

   Pages 65-84

6. Order-of-Magnitude Popularity Estimation of Pirated Content

   • Charalampos Chelmis, Daphney-Stavroula Zois

   Pages 85-113

7. Learning What to Share in Online Social Networks Using Deep Reinforcement Learning

   • Shatha Jaradat, Nima Dokoohaki, Mihhail Matskin, Elena Ferrari

   Pages 115-133

8. Centrality and Community Scoring Functions in Incomplete Networks: Their Sensitivity, Robustness, and Reliability

   • Soumya Sarkar, Suhansanu Kumar, Sanjukta Bhowmick, Animesh Mukherjee

   Pages 135-154

9. Ameliorating Search Results Recommendation System Based on K-Means Clustering Algorithm and Distance Measurements

   • Marwa Massaâbi, Olfa Layouni, Jalel Akaichi

   Pages 155-172

10. Dynamics of Large-Scale Networks Following a Merger

    • John Clements, Babak Farzad, Henryk Fukś

    Pages 173-193

11. Cloud Assisted Personal Online Social Network

    • Esra Erdin, Eric Klukovich, Mehmet Hadi Gunes

    Pages 195-218

12. Text-Based Analysis of Emotion by Considering Tweets

    • Kashfia Sailunaz, Tansel Özyer, Jon Rokne, Reda Alhajj

    Pages 219-236

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. 

• graph analysis
• online social network
• deep learning
• data analysis
• functional cluster extraction
• computational social sciences

• Department of Computer Engineering, TOBB University of Economics and Technology, Ankara, Turkey

  Tansel Özyer

• Department of Computer Science, University of Calgary, Calgary, Canada

  Reda Alhajj

Tansel Özyer is an associate professor of Computer Engineering at TOBB University of Economics and Technology, Turkey. He completed his PhD in Computer Science, University of Calgary. He received his MSc and BSc from Computer Engineering departments of METU and Bilkent University. Research interests are data mining, social network analysis, machine learning, bioinformatics, XML, mobile databases, and computer vision.

Reda Alhajj is a professor in the Department of Computer Science at the University of Calgary. He published over 500 papers in refereed international journals and conferences. He is founding editor in chief of the Springer premier journal “Social Networks Analysis and Mining”, founding editor-in-chief of Springer Series “Lecture Notes on Social Networks”, founding editor-in-chief of Springer journal “Network Modeling Analysis in Health Informatics and Bioinformatics”, founding co-editor-in-chief of Springer “Encyclopedia on Social NetworksAnalysis and Mining”, founding steering chair of IEEE/ACM ASONAM, and three accompanying symposiums FAB, FOSINT-SI and HI-BI-BI. Dr. Alhajj's research concentrates primarily on data science from management to integration and analysis.

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