Fr. 130.00

Machine Learning Techniques for Online Social Networks

Englisch · Taschenbuch

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Beschreibung

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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.

Inhaltsverzeichnis

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.

Über den Autor / die Autorin










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.


Zusammenfassung

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. 

Produktdetails

Mitarbeit Alhajj (Herausgeber), Reda Alhajj (Herausgeber), Tanse Özyer (Herausgeber), Tansel Özyer (Herausgeber)
Verlag Springer, Berlin
 
Sprache Englisch
Produktform Taschenbuch
Erschienen 01.01.2018
 
EAN 9783030078966
ISBN 978-3-0-3007896-6
Seiten 236
Gewicht 379 g
Illustration 17 SW-Abb., 85 Farbabb.
Serie Lecture Notes in Social Networks
Themen Sozialwissenschaften, Recht,Wirtschaft > Soziologie > Methoden der empirischen und qualitativen Sozialforschung

B, Social Media, Data Mining, Media Studies, Artificial Intelligence, biotechnology, Social Sciences, Data Mining and Knowledge Discovery, Expert systems / knowledge-based systems, Social sciences—Computer programs, Social sciences—Data processing, Computational Social Sciences

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