東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Elements of network science = theory...

Zinilli, Antonio.

Linked to FindBook

Google Book

Amazon

博客來

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

Record Type:	Electronic resources : Monograph/item
Title/Author:	Elements of network science/ by Antonio Zinilli.
Reminder of title:	theory, methods and applications in Stata, R and Python /
Author:	Zinilli, Antonio.
Published:	Cham :Springer Nature Switzerland : : 2025.,
Description:	xvi, 242 p. :ill. (chiefly color), digital ;24 cm.
[NT 15003449]:	- 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
Subject:	System analysis - Computer programs. -
Online resource:	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 /
LDR:03139nmm a2200337 a 4500 001 2410403
003 DE-He213
005 20250429130219.0
006 m d
007 cr nn 008maaau
008 260204s2025 sz s 0 eng d
020 $a 9783031847127 $q (electronic bk.)
020 $a 9783031847110 $q (paper)
024 7 $a 10.1007/978-3-031-84712-7 $2 doi
035 $a 978-3-031-84712-7
040 $a GP $c GP
041 0 $a eng
050 4 $a QA402
072 7 $a JHBC $2 bicssc
072 7 $a SOC027000 $2 bisacsh
072 7 $a JHBC $2 thema
082 0 4 $a 003.3 $2 23
090 $a QA402 $b .Z77 2025
100 1 $a Zinilli, Antonio. $3 3784265
245 1 0 $a Elements of network science $h [electronic resource] : $b theory, methods and applications in Stata, R and Python / $c by Antonio Zinilli.
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2025.
300 $a xvi, 242 p. : $b ill. (chiefly color), digital ; $c 24 cm.
490 1 $a Statistics and computing, $x 2197-1706
505 0 $a - 1. Introduction -- 2. Network Science: concepts and definitions -- 3. Network Metrics -- 4. Theoretical models of networks -- 5.Statistical social network models.
520 $a 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.
630 0 0 $a Stata. $3 783375
650 0 $a System analysis $x Computer programs. $3 3784266
650 0 $a System analysis $x Statistical methods. $3 1005919
650 0 $a R (Computer program language) $3 784593
650 0 $a Python (Computer program language) $3 729789
650 1 4 $a Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy. $3 3538811
650 2 4 $a Statistics and Computing. $3 3594429
650 2 4 $a Network Research. $3 3595885
650 2 4 $a Statistical Software. $3 3596845
650 2 4 $a Statistics in Business, Management, Economics, Finance, Insurance. $3 3538572
650 2 4 $a Statistical Theory and Methods. $3 891074
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Statistics and computing. $3 1566755
856 4 0 $u https://doi.org/10.1007/978-3-031-84712-7
950 $a Mathematics and Statistics (SpringerNature-11649)