Editors:
- Presents views and results from leading experts in network science
- Contains many illustrations, tables, as well as pseudocode samples
Part of the book series: Springer Proceedings in Complexity (SPCOM)
Conference series link(s): DOOCN: Dynamics on and of Complex Networks
Conference proceedings info: DOOCN 2017.
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Table of contents (10 papers)
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Front Matter
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Back Matter
About this book
The book contains extended versions of high-quality submissions received at the workshop, Dynamics On and Of Complex Networks (doocn.org), together with new invited contributions. The chapters will benefit a diverse community of researchers. The book is suitable for graduate students, postdoctoral researchers and professors of various disciplines including sociology, physics, mathematics, and computer science.
Editors and Affiliations
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Institute of Theoretical Physics, Technical University of Berlin, Berlin, Germany
Fakhteh Ghanbarnejad
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Max Planck Institute for Informatics, Saarbrücken, Germany
Rishiraj Saha Roy
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Department of Computational Social Science, GESIS, Leibniz Institute for the Social Science, Köln, Germany
Fariba Karimi
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Université Catholique de Louvain, Louvain-la-Neuve, Belgium
Jean-Charles Delvenne
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Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India
Bivas Mitra
Bibliographic Information
Book Title: Dynamics On and Of Complex Networks III
Book Subtitle: Machine Learning and Statistical Physics Approaches
Editors: Fakhteh Ghanbarnejad, Rishiraj Saha Roy, Fariba Karimi, Jean-Charles Delvenne, Bivas Mitra
Series Title: Springer Proceedings in Complexity
DOI: https://doi.org/10.1007/978-3-030-14683-2
Publisher: Springer Cham
eBook Packages: Physics and Astronomy, Physics and Astronomy (R0)
Copyright Information: Springer Nature Switzerland AG 2019
Hardcover ISBN: 978-3-030-14682-5Published: 14 May 2019
Softcover ISBN: 978-3-030-14685-6Published: 14 August 2020
eBook ISBN: 978-3-030-14683-2Published: 13 May 2019
Series ISSN: 2213-8684
Series E-ISSN: 2213-8692
Edition Number: 1
Number of Pages: X, 244
Number of Illustrations: 8 b/w illustrations, 68 illustrations in colour
Topics: Data-driven Science, Modeling and Theory Building, Complexity, Computational Social Sciences, Complex Systems