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.
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.
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.
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.
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