Dynamics On and Of Complex Networks III Machine Learning and Statist
This book bridges the gap between advances in the communities of computer science and physics--namely machine learning and statistical physics. It contains diverse but relevant topics in statistical physics, complex systems, network theory, and machine le
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Fakhteh Ghanbarnejad Rishiraj Saha Roy Fariba Karimi Jean-Charles Delvenne Bivas Mitra Editors
Dynamics On and Of Complex Networks III Machine Learning and Statistical Physics Approaches
Springer Proceedings in Complexity
Springer Proceedings in Complexity publishes proceedings from scholarly meetings on all topics relating to the interdisciplinary studies of complex systems science. Springer welcomes book ideas from authors. The series is indexed in Scopus. Proposals must include the following: – name, place and date of the scientific meeting – a link to the committees (local organization, international advisors etc.) – scientific description of the meeting – list of invited/plenary speakers – an estimate of the planned proceedings book parameters (number of pages/articles, requested number of bulk copies, submission deadline). submit your proposals to: [email protected]
More information about this series at http://www.springer.com/series/11637
Fakhteh Ghanbarnejad • Rishiraj Saha Roy Fariba Karimi • Jean-Charles Delvenne Bivas Mitra Editors
Dynamics On and Of Complex Networks III Machine Learning and Statistical Physics Approaches
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Editors Fakhteh Ghanbarnejad Institute of Theoretical Physics Technical University of Berlin Berlin, Germany Fariba Karimi Department of Computational Social Science GESIS Leibniz Institute for the Social Science K¨oln, Germany
Rishiraj Saha Roy Max Planck Institute for Informatics Saarbr¨ucken, Germany
Jean-Charles Delvenne Université Catholique de Louvain Louvain-la-Neuve, Belgium
Bivas Mitra Department of Computer Science and Engineering Indian Institute of Technology Kharagpur Kharagpur, India
ISSN 2213-8684 ISSN 2213-8692 (electronic) Springer Proceedings in Complexity ISBN 978-3-030-14682-5 ISBN 978-3-030-14683-2 (eBook) https://doi.org/10.1007/978-3-030-14683-2 © Springer Nature Switzerland AG 2019 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 jurisdictiona
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