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

Social network computing

Wu, Jiang.

Linked to FindBook

Google Book

Amazon

博客來

Social network computing

Record Type:	Electronic resources : Monograph/item
Title/Author:	Social network computing/ by Jiang Wu.
Author:	Wu, Jiang.
Published:	Singapore :Springer Nature Singapore : : 2025.,
Description:	xxv, 627 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Chapter 1 Introduction to Social Network Computing -- Chapter 2 Visualization of Social Networks -- Chapter 3 Triadic Closure in Social Networks -- Chapter 4 Strong and Weak Relationships in Social Networks -- Chapter 5 Homophily in Social Networks -- Chapter 6 Positive and Negative Balance in Social Networks -- Chapter 7 The Small World in Social Networks -- Chapter 8 Power Laws in Social Networks -- Chapter 9 Communities in Social Networks -- Chapter 10 Communication in Social Networks -- Chapter 11 Games in Social Networks -- Chapter 12 Networks in Social Networks -- Chapter 13 Link Prediction for Social Networks -- Chapter 14 Evaluation of the Influence of Social Networks -- Chapter 15 Dynamic Analysis of Social Networks -- Chapter 16 Randomized Experiments in Social Networks -- Chapter 17 Modeling and Simulation of Social Networks -- Chapter 18 Representation Learning for Social Networks.
Contained By:	Springer Nature eBook
Subject:	Social networks - Data processing. -
Online resource:	https://doi.org/10.1007/978-981-97-4084-0
ISBN:	9789819740840

Social network computing
Wu, Jiang.

Social network computing[electronic resource] /by Jiang Wu. - Singapore :Springer Nature Singapore :2025. - xxv, 627 p. :ill. (some col.), digital ;24 cm.

Chapter 1 Introduction to Social Network Computing -- Chapter 2 Visualization of Social Networks -- Chapter 3 Triadic Closure in Social Networks -- Chapter 4 Strong and Weak Relationships in Social Networks -- Chapter 5 Homophily in Social Networks -- Chapter 6 Positive and Negative Balance in Social Networks -- Chapter 7 The Small World in Social Networks -- Chapter 8 Power Laws in Social Networks -- Chapter 9 Communities in Social Networks -- Chapter 10 Communication in Social Networks -- Chapter 11 Games in Social Networks -- Chapter 12 Networks in Social Networks -- Chapter 13 Link Prediction for Social Networks -- Chapter 14 Evaluation of the Influence of Social Networks -- Chapter 15 Dynamic Analysis of Social Networks -- Chapter 16 Randomized Experiments in Social Networks -- Chapter 17 Modeling and Simulation of Social Networks -- Chapter 18 Representation Learning for Social Networks.

In the era of digital economy with highly-connected world, the ability to comprehend social network computing has become an indispensable skill. This book serves as a vital guide for academics and professionals engaged in research within this rapidly expanding field. The book is organized into three parts, each dedicated to different facets of social network computing. The journey commences with an exploration of foundational principles, encompassing triadic closure, strong and weak ties, network homophily, and positive and negative balance. This lays the groundwork for an in-depth examination of fundamental theories governing social networks. Subsequently, the focus shifts to the laws dictating social networks, elucidating phenomena like the small world effect, power law distribution, community detection, diffusion processes, game theory dynamics, and hypernetworks, also including multiplex networks, multi-mode networks and temporal networks. The final section of the book centers on the practical aspects of social network analysis, delving into topics such as link prediction, influence evaluation, dynamic analysis, random experiments, modeling and simulation, and representation learning. This provides a comprehensive exploration of the applicability of social network analysis in real-world scenarios. This book seamlessly integrates theory with practice by incorporating popular social network computing software, including igraph, Gephi, Ucinet, and Netlogo. By mastering the computational thinking methods presented in this book, readers will deepen their understanding of social network computing and acquire the skills to effectively apply it in various real-world contexts.

ISBN: 9789819740840

Standard No.: 10.1007/978-981-97-4084-0doiSubjects--Topical Terms:

1066424
Social networks
--Data processing.

LC Class. No.: H61.3

Dewey Class. No.: 302.30285

Social network computing
LDR:03547nmm a2200325 a 4500 001 2407738
003 DE-He213
005 20241101115733.0
006 m d
007 cr nn 008maaau
008 260204s2025 si s 0 eng d
020 $a 9789819740840 $q (electronic bk.)
020 $a 9789819740833 $q (paper)
024 7 $a 10.1007/978-981-97-4084-0 $2 doi
035 $a 978-981-97-4084-0
040 $a GP $c GP
041 0 $a eng
050 4 $a H61.3
072 7 $a UF $2 bicssc
072 7 $a COM005000 $2 bisacsh
072 7 $a UXJ $2 thema
082 0 4 $a 302.30285 $2 23
090 $a H61.3 $b .W959 2025
100 1 $a Wu, Jiang. $3 1297057
245 1 0 $a Social network computing $h [electronic resource] / $c by Jiang Wu.
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2025.
300 $a xxv, 627 p. : $b ill. (some col.), digital ; $c 24 cm.
505 0 $a Chapter 1 Introduction to Social Network Computing -- Chapter 2 Visualization of Social Networks -- Chapter 3 Triadic Closure in Social Networks -- Chapter 4 Strong and Weak Relationships in Social Networks -- Chapter 5 Homophily in Social Networks -- Chapter 6 Positive and Negative Balance in Social Networks -- Chapter 7 The Small World in Social Networks -- Chapter 8 Power Laws in Social Networks -- Chapter 9 Communities in Social Networks -- Chapter 10 Communication in Social Networks -- Chapter 11 Games in Social Networks -- Chapter 12 Networks in Social Networks -- Chapter 13 Link Prediction for Social Networks -- Chapter 14 Evaluation of the Influence of Social Networks -- Chapter 15 Dynamic Analysis of Social Networks -- Chapter 16 Randomized Experiments in Social Networks -- Chapter 17 Modeling and Simulation of Social Networks -- Chapter 18 Representation Learning for Social Networks.
520 $a In the era of digital economy with highly-connected world, the ability to comprehend social network computing has become an indispensable skill. This book serves as a vital guide for academics and professionals engaged in research within this rapidly expanding field. The book is organized into three parts, each dedicated to different facets of social network computing. The journey commences with an exploration of foundational principles, encompassing triadic closure, strong and weak ties, network homophily, and positive and negative balance. This lays the groundwork for an in-depth examination of fundamental theories governing social networks. Subsequently, the focus shifts to the laws dictating social networks, elucidating phenomena like the small world effect, power law distribution, community detection, diffusion processes, game theory dynamics, and hypernetworks, also including multiplex networks, multi-mode networks and temporal networks. The final section of the book centers on the practical aspects of social network analysis, delving into topics such as link prediction, influence evaluation, dynamic analysis, random experiments, modeling and simulation, and representation learning. This provides a comprehensive exploration of the applicability of social network analysis in real-world scenarios. This book seamlessly integrates theory with practice by incorporating popular social network computing software, including igraph, Gephi, Ucinet, and Netlogo. By mastering the computational thinking methods presented in this book, readers will deepen their understanding of social network computing and acquire the skills to effectively apply it in various real-world contexts.
650 0 $a Social networks $x Data processing. $3 1066424
650 1 4 $a Computer Application in Social and Behavioral Sciences. $3 3538516
650 2 4 $a Computer and Information Systems Applications. $3 3538505
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-981-97-4084-0
950 $a Computer Science (SpringerNature-11645)