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Full Description
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
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.
