Machine Learning Techniques for Online Social Networks
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. Th
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Tansel Özyer Reda Alhajj Editors
Machine Learning Techniques for Online Social Networks
Lecture Notes in Social Networks Series editors Reda Alhajj, University of Calgary, Calgary, AB, Canada Uwe Glässer, Simon Fraser University, Burnaby, BC, Canada Huan Liu, Arizona State University, Tempe, AZ, USA Rafael Wittek, University of Groningen, Groningen, The Netherlands Daniel Zeng, University of Arizona, Tucson, AZ, USA Advisory Board Charu C. Aggarwal, Yorktown Heights, NY, USA Patricia L. Brantingham, Simon Fraser University, Burnaby, BC, Canada Thilo Gross, University of Bristol, Bristol, UK Jiawei Han, University of Illinois at Urbana-Champaign, Urbana, IL, USA Raúl Manásevich, University of Chile, Santiago, Chile Anthony J. Masys, University of Leicester, Ottawa, ON, Canada Carlo Morselli, School of Criminology, Montreal, QC, Canada
More information about this series at http://www.springer.com/series/8768
Tansel Özyer • Reda Alhajj Editors
Machine Learning Techniques for Online Social Networks
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Editors Tansel Özyer Department of Computer Engineering TOBB University of Economics and Technology Ankara, Turkey
Reda Alhajj Department of Computer Science University of Calgary Calgary, AB, Canada
ISSN 2190-5428 ISSN 2190-5436 (electronic) Lecture Notes in Social Networks ISBN 978-3-319-89931-2 ISBN 978-3-319-89932-9 (eBook) https://doi.org/10.1007/978-3-319-89932-9 Library of Congress Control Number: 2018943402 © Springer International Publishing AG, part of Springer Nature 2018 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Printed on acid-free paper This Springer imprint is published by the registered company Springer International Publishing AG part of Springer Nature. The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
Preface
Machine learning techniques are essential for social network analysis leading to effective
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