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Elements of network science = theory...

Zinilli, Antonio.

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Elements of network science = theory, methods and applications in Stata, R and Python /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Elements of network science/ by Antonio Zinilli.
其他題名:	theory, methods and applications in Stata, R and Python /
作者:	Zinilli, Antonio.
出版者:	Cham :Springer Nature Switzerland : : 2025.,
面頁冊數:	xvi, 242 p. :ill. (chiefly color), digital ;24 cm.
內容註:	- 1. Introduction -- 2. Network Science: concepts and definitions -- 3. Network Metrics -- 4. Theoretical models of networks -- 5.Statistical social network models.
Contained By:	Springer Nature eBook
標題:	System analysis - Computer programs. -
電子資源:	https://doi.org/10.1007/978-3-031-84712-7
ISBN:	9783031847127

Elements of network science = theory, methods and applications in Stata, R and Python /
Zinilli, Antonio.

Elements of network sciencetheory, methods and applications in Stata, R and Python /[electronic resource] :by Antonio Zinilli. - Cham :Springer Nature Switzerland :2025. - xvi, 242 p. :ill. (chiefly color), digital ;24 cm. - Statistics and computing,2197-1706. - Statistics and computing..

- 1. Introduction -- 2. Network Science: concepts and definitions -- 3. Network Metrics -- 4. Theoretical models of networks -- 5.Statistical social network models.

This book provides readers with a comprehensive guide to designing rigorous and effective network science tools using the statistical software platforms Stata, R, and Python. Network science offers a means to understand and analyze complex systems that involve various types of relationships. This text bridges the gap between theoretical understanding and practical application, making network science more accessible to a wide range of users. It presents the statistical models pertaining to individual network techniques, followed by empirical applications that use both built-in and user-written packages, and reveals the mathematical and statistical foundations of each model, along with demonstrations involving calculations and step-by-step code implementation. In addition, each chapter is complemented by a case study that illustrates one of the several techniques discussed. The introductory chapter serves as a roadmap for readers, providing an initial understanding of network science and guidance on the required packages, the second chapter focuses on the main concepts related to network properties. The next two chapters present the primary definitions and concepts in network science and various classes of graphs observed in real contexts. The final chapter explores the main social network models, including the family of exponential random graph models. Each chapter includes real-world data applications from the social sciences, using at least one of the platforms Stata, R, and Python, providing a more comprehensive understanding of the availability of network science methods across different software platforms. The underlying computer code and data sets are available online. The book will appeal to graduate students, researchers and data scientists, mainly from the social sciences, who seek theoretical and applied tools to implement network science techniques in their work.

ISBN: 9783031847127

Standard No.: 10.1007/978-3-031-84712-7doiSubjects--Uniform Titles:

Stata.
Subjects--Topical Terms:

3784266
System analysis
--Computer programs.

LC Class. No.: QA402

Dewey Class. No.: 003.3

Elements of network science = theory, methods and applications in Stata, R and Python /
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