Dynamics On and Of Complex Networks III

Machine Learning and Statistical Physics Approaches
1. Auflage 2019
ISBN: 978-3-030-14682-5
Verlag: Springer International Publishing

Buch, Englisch, 244 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 553 g

Reihe: Springer Proceedings in Complexity

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.

Zielgruppe

Research

Autoren/Hrsg.

Ghanbarnejad, Fakhteh

Saha Roy, Rishiraj

Mitra, Bivas

Karimi, Fariba

Delvenne, Jean-Charles

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik Mathematik Mathematik Interdisziplinär Systemtheorie
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Computeranwendungen in Geistes- und Sozialwissenschaften
• Mathematik | Informatik EDV | Informatik Informatik Berechenbarkeitstheorie, Komplexitätstheorie
• Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Kybernetik, Systemtheorie, Komplexe Systeme
• Naturwissenschaften Physik Angewandte Physik Statistische Physik, Dynamische Systeme

Inhaltsverzeichnis

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.