Working with Network Data
portes grátis
Working with Network Data
A Data Science Perspective
Bagrow, James; Ahn, Yong-Yeol
Cambridge University Press
05/2024
400
Dura
9781009212595
Pré-lançamento - envio 15 a 20 dias após a sua edição
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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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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.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
