Dynamics On and Of Complex Networks III -

Dynamics On and Of Complex Networks III

Machine Learning and Statistical Physics Approaches
Buch | Hardcover
X, 244 Seiten
2019 | 1st ed. 2019
Springer International Publishing (Verlag)
978-3-030-14682-5 (ISBN)
160,49 inkl. MwSt
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.

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.

 

Erscheinungsdatum
Reihe/Serie Springer Proceedings in Complexity
Zusatzinfo X, 244 p. 76 illus., 68 illus. in color.
Verlagsort Cham
Sprache englisch
Maße 155 x 235 mm
Gewicht 541 g
Themenwelt Mathematik / Informatik Informatik Theorie / Studium
Mathematik / Informatik Mathematik Angewandte Mathematik
Naturwissenschaften Physik / Astronomie Theoretische Physik
Sozialwissenschaften Soziologie Empirische Sozialforschung
Schlagworte community detection • Complexity • Computational Social Sciences • Data-driven Science, Modeling and Theory Building • generating random networks • inferring network structure • Information Diffusion • nodes in empirical networks • nonlinear dynamics on networks
ISBN-10 3-030-14682-0 / 3030146820
ISBN-13 978-3-030-14682-5 / 9783030146825
Zustand Neuware
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