Dynamics On and Of Complex Networks III / Springer Proceedings in Complexity (PDF)
Machine Learning and Statistical Physics Approaches
(Sprache: Englisch)
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...
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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 learning. Examples of such topics are: predicting missing links, higher-order generative modeling of networks, inferring network structure by tracking the evolution and dynamics of digital traces, recommender systems, and diffusion processes.
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.
Bibliographische Angaben
- 2019, 1st ed. 2019, 244 Seiten, Englisch
- Herausgegeben: Fakhteh Ghanbarnejad, Rishiraj Saha Roy, Fariba Karimi, Jean-Charles Delvenne, Bivas Mitra
- Verlag: Springer-Verlag GmbH
- ISBN-10: 3030146839
- ISBN-13: 9783030146832
- Erscheinungsdatum: 13.05.2019
Abhängig von Bildschirmgröße und eingestellter Schriftgröße kann die Seitenzahl auf Ihrem Lesegerät variieren.
eBook Informationen
- Dateiformat: PDF
- Größe: 9.80 MB
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- Vorlesefunktion
Sprache:
Englisch
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