Machine Learning Techniques for Online Social Networks

Buch | Hardcover

VIII, 236 Seiten

2018
Springer International Publishing (Verlag)
978-3-319-89931-2 (ISBN)

Artikel merken

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.

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 Networks Analysis 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.

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.

Erscheinungsdatum	09.06.2018
Reihe/Serie	Lecture Notes in Social Networks
Zusatzinfo	VIII, 236 p. 102 illus., 85 illus. in color.
Verlagsort	Cham
Sprache	englisch
Maße	155 x 235 mm
Gewicht	532 g
Themenwelt	Sozialwissenschaften ► Soziologie ► Empirische Sozialforschung
Schlagworte	Computational Social Sciences • Data Analysis • Deep learning • functional cluster extraction • graph analysis • online social network
ISBN-10	3-319-89931-7 / 3319899317
ISBN-13	978-3-319-89931-2 / 9783319899312
Zustand	Neuware