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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.
Produkteigenschaften
- Artikelnummer: 9783030146856
- Medium: Buch
- ISBN: 978-3-030-14685-6
- Verlag: Springer International Publishing
- Erscheinungstermin: 14.08.2020
- Sprache(n): Englisch
- Auflage: 1. Auflage 2019
- Serie: Springer Proceedings in Complexity
- Produktform: Kartoniert
- Gewicht: 394 g
- Seiten: 244
- Format (B x H x T): 155 x 235 x 15 mm
- Ausgabetyp: Kein, Unbekannt
Themen
- Interdisziplinäres
- Wissenschaften
- Wissenschaften: Forschung und Information
- Kybernetik, Systemtheorie, Komplexe Systeme
- Mathematik | Informatik
- EDV | Informatik
- Angewandte Informatik
- Computeranwendungen in Geistes- und Sozialwissenschaften