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Dynamics On and Of Complex Networks III : Machine Learning and Statistical Physics Approaches / edited by Fakhteh Ghanbarnejad, Rishiraj Saha Roy, Fariba Karimi, Jean-Charles Delvenne, Bivas Mitra.

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Format:
Book
Contributor:
Ghanbarnejad, Fakhteh, editor.
Saha Roy, Rishiraj, editor.
Karimi, Fariba, editor.
Delvenne, Jean-Charles, editor.
Mitra, Bivas, editor.
SpringerLink (Online service)
Series:
Springer proceedings in complexity 2213-8684
Springer Proceedings in Complexity, 2213-8684
Language:
English
Subjects (All):
Engineering.
Social sciences--Data processing.
Social sciences.
Social sciences--Computer programs.
Data-driven Science, Modeling and Theory Building.
Complexity.
Computational Social Sciences.
Complex Systems.
Local Subjects:
Data-driven Science, Modeling and Theory Building.
Complexity.
Computational Social Sciences.
Complex Systems.
Physical Description:
1 online resource (X, 244 pages) : 76 illustrations, 68 illustrations in color.
Contained In:
Springer eBooks
Place of Publication:
Cham : Springer International Publishing : Imprint: Springer, 2019.
System Details:
text file PDF
Summary:
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.
Contents:
Part1. Network Structure
Chapter1. An Empirical Study of the Effect of Noise Models on Centrality Metrics
Chapter2. Emergence and Evolution of Hierarchical Structure in Complex Systems
Chapter3. Evaluation of Cascading Infrastructure Failures and Optimal Recovery from a Network Science Perspective
Part2. Network Dynamics
Chapter4. Automatic Discovery of Families of Network Generative Processes
Chapter5. Modeling User Dynamics in Collaboration Websites
Chapter6. The Problem of Interaction Prediction in Link Streams
Chapter7. The Network Source Location Problem in the Context of Foodborne Disease Outbreaks
Part3. Theoretical Models and applications
Chapter8. Network Representation Learning using Local Sharing and Distributed Graph Factorization (LSDGF)
Chapter9. The Anatomy of Reddit: An Overview of Academic Research
Chapter10. Learning Information Dynamics in Social Media: A Temporal Point Process Perspective. .
Other Format:
Printed edition:
ISBN:
978-3-030-14683-2
9783030146832
Access Restriction:
Restricted for use by site license.

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