Dynamics On and Of Complex Networks III Machine Learning and Statistical Physics Approaches
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: predicti...
| Other Authors: | , , , |
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| Format: | eBook |
| Language: | English |
| Published: |
Cham
Springer International Publishing
2019, 2019
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| Edition: | 1st ed. 2019 |
| Series: | Springer Proceedings in Complexity
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| Subjects: | |
| Online Access: | |
| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
Table of 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.