Dynamics On and Of Complex Networks III

Machine Learning and Statistical Physics Approaches

Fakhteh Ghanbarnejad, Rishiraj Saha Roy, Fariba Karimi, Jean-Charles Delvenne, Bivas Mitra (Herausgeber)

Buch | Hardcover

X, 244 Seiten

2019
Springer International Publishing (Verlag)
978-3-030-14682-5 (ISBN)

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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.

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	15.05.2019
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