Machine Learning Techniques for Online Social Networks



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Éditeur :

Springer


Paru le : 2018-05-30



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Description

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. 


Pages
236 pages
Collection
n.c
Parution
2018-05-30
Marque
Springer
EAN papier
9783319899312
EAN PDF
9783319899329

Informations sur l'ebook
Nombre pages copiables
2
Nombre pages imprimables
23
Taille du fichier
10179 Ko
Prix
96,29 €
EAN EPUB
9783319899329

Informations sur l'ebook
Nombre pages copiables
2
Nombre pages imprimables
23
Taille du fichier
74179 Ko
Prix
96,29 €

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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