Working with Network Data
A Data Science Perspective

Suitable for students and researchers in a range of disciplines, this novel text provides a fast-track to network data expertise.

James Bagrow (Author), Yong‐Yeol Ahn (Author)

9781009212595, Cambridge University Press

Hardback, published 13 June 2024

554 pages
25.1 x 17.7 x 3.5 cm, 1.13 kg

'An essential resource for newcomers to network science, this book expertly addresses the practical challenges of handling network data. Through a rich array of real-world examples and hands-on exercises, Bagrow and Ahn skillfully guide readers through the complexities of conceptualizing and analyzing networked data, making this text a fundamental tool for students and researchers eager to explore the power of connections across various disciplines.' Albert-László Barabási, Dodge Distinguished Professor of Network Science at Northeastern University

Drawing examples from real-world networks, this essential book traces the methods behind network analysis and explains how network data is first gathered, then processed and interpreted. The text will equip you with a toolbox of diverse methods and data modelling approaches, allowing you to quickly start making your own calculations on a huge variety of networked systems. This book sets you up to succeed, addressing the questions of what you need to know and what to do with it, when beginning to work with network data. The hands-on approach adopted throughout means that beginners quickly become capable practitioners, guided by a wealth of interesting examples that demonstrate key concepts. Exercises using real-world data extend and deepen your understanding, and develop effective working patterns in network calculations and analysis. Suitable for both graduate students and researchers across a range of disciplines, this novel text provides a fast-track to network data expertise.

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.

Subject Areas: Statistical physics [PHS]