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Working with Network Data A Data Science Perspective

Bagrow, James Ahn, Yong‐Yeol
Innbundet / 2024 / Engelsk

Produktdetaljer

ISBN
9781009212595
Publisert
2024-06-13
Utgiver
Vendor
Cambridge University Press
Vekt
1130 gr
Høyde
251 mm
Bredde
177 mm
Dybde
35 mm
Aldersnivå
G, 01
Språk
Product language
Engelsk
Format
Product format
Innbundet
Antall sider
554

Forfatter
Bagrow, James
Ahn, Yong‐Yeol

Biographical note

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. Yong-Yeol Ahn is Professor at Indiana University and a former Visiting Professor at the Massachusetts Institute of Technology. He specializes in network and data science and machine learning, and his research on complex social and biological systems has been recognized by many awards, including the Microsoft Research Faculty Fellowship.

Working with Network Data A Data Science Perspective

Bagrow, James Ahn, Yong‐Yeol
Innbundet / 2024 / Engelsk
Bagrow, James Ahn, Yong‐Yeol
Innbundet / 2024 / Engelsk
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'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.
Les mer
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.
Les mer
Suitable for students and researchers in a range of disciplines, this novel text provides a fast-track to network data expertise.

Relaterte produkter

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

Produktdetaljer

ISBN
9781009212595
Publisert
2024-06-13
Utgiver
Vendor
Cambridge University Press
Vekt
1130 gr
Høyde
251 mm
Bredde
177 mm
Dybde
35 mm
Aldersnivå
G, 01
Språk
Product language
Engelsk
Format
Product format
Innbundet
Antall sider
554

Forfatter
Bagrow, James
Ahn, Yong‐Yeol

Biographical note

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. Yong-Yeol Ahn is Professor at Indiana University and a former Visiting Professor at the Massachusetts Institute of Technology. He specializes in network and data science and machine learning, and his research on complex social and biological systems has been recognized by many awards, including the Microsoft Research Faculty Fellowship.

Relaterte produkter