James Bagrow (University of Vermont), Yong-Yeol Ahn (Bloomington Indiana University)

Working with Network Data

A Data Science Perspective

• Gebundenes Buch

Produktbeschreibung

Drawing examples from real-world networks, this essential book traces the methods behind network analysis and equips you with a toolbox of diverse methods and data modelling approaches. Suitable for both graduate students and researchers across a range of disciplines, this novel text provides a fast-track to network data expertise.

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Produktdetails

• Verlag: Cambridge University Press
• Seitenzahl: 554
• Erscheinungstermin: 13. Juni 2024
• Englisch
• Abmessung: 248mm x 176mm x 39mm
• Gewicht: 1124g
• ISBN-13: 9781009212595
• ISBN-10: 1009212591
• Artikelnr.: 69868860

Autorenporträt

James Bagrow is Associate Professor in Mathematics & Statistics at the University of Vermont. He works at the intersection of data science, complex systems and applied mathematics, using cutting-edge methods, mathematical models and large-scale data to explore and understand complex networks and systems.

Inhaltsangabe

Contents
Preface
Part I. Background: 1. A whirlwind tour of network science
2. Network data across fields
3. Data ethics
4. Primer
Part II. Applications, Tools and Tasks: 5. The life-cycle of a network study
6. Gathering data
7. Extracting networks from data - the 'upstream task'
8. Implementation: storing and manipulating network data
9. Incorporating node and edge attributes
10. Awful errors and how to amend them
11. Explore and explain: statistics for network data
12. Understanding network structure and organization
13. Visualizing networks
14. Summarizing and comparing networks
15. Dynamics and dynamic networks
16. Machine learning
Interlude - Good practices for scientific computing
17. Research record-keeping
18. Data provenance
19. Reproducible and reliable code
20. Helpful tools
Part III. Fundamentals: 21. Networks demand network thinking: the friendship paradox
22. Network models
23. Statistical models and inference
24. Uncertainty quantification and error analysis
25. Ghost in the matrix: spectral methods for networks
26. Embedding and machine learning
27. Big data and scalability
Conclusion
Bibliography
Index.