Tools

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001 on1552066249;

003 OCoLC;

005 20260705181057.6;

007 t|;

008 260226s2026 sz a 000 0 eng d;

020 a| 9783032148322;

020 a| 3032148324;

035 a| (OCoLC)1552066249;

050 4 a| HM742b| .W8 2026;

082 04 a| 302.302852| 23;

100 1 a| Wu, Weili,e| author.1| https://isni.org/isni/0000000115713714.?| UNAUTHORIZED;

245 10 a| Computational aspects of social networks /c| Weili Wu, Zhao Zhang, Ding-Zhu Du.;

264 1 a| Cham :b| Springer,c| 2026.;

300 a| 409 pages :b| illustrations (black and white, and colour) ;c| 24 cm.;

336 a| text2| rdacontent;

336 a| still image2| rdacontent;

337 a| unmediated2| rdamedia;

338 a| volume2| rdacarrier;

490 0 a| Springer optimization and its applications ;;

505 0 a| Introduction and community -- Social influence -- Monte Carlo method -- Viral marketing -- Nonsubmodular optimization -- Local optimum -- Rumor -- Competitive influence -- Adaptive influence -- Robust and machine learning -- Deep learning in social networks.;

520 a| This book offers a comprehensive exploration of computational social networks, focusing on the algorithmic and optimization aspects crucial for applications across diverse domains. As social data proliferates through platforms like Facebook, LinkedIn, and Skype, the need for efficient techniques to extract meaningful insights becomes paramount. This volume provides readers with a robust foundation in computational methods tailored for social networks. Key concepts include combinatorial optimization, machine learning applications, and advanced computational techniques. The chapters are meticulously organized to guide readers through fundamental knowledge, optimization strategies, and cutting-edge topics in the field. By integrating lecture notes and selected materials from leading IEEE/ACM publications, this book serves as an essential resource for understanding the complexities of social data analysis. Designed for graduate and senior undergraduate students in computer science and applied mathematics, this book assumes a foundational knowledge of programming and algorithm design. It is an invaluable tool for those seeking to harness the power of computational social networks in fields such as public safety, viral marketing, and misinformation clarification.;

650 0 a| Online social networksx| Data processing.=| ^A708333;

650 7 a| Online social networks2| fast;

700 1 a| Du, Dingzhu,e| author.1| https://isni.org/isni/0000000108914462.=| ^A358957;

949 a| Order on Demandw| ASISh| JOYNER219;

960 o| 1s| 69.99u| JSOCz| USD;

961 f| DMDm| 138099;

596 a| 1;

998 a| 6736616;

999 a| ORDER ON DEMANDw| ASISc| 1i| 6736616-1001l| JYBPDDAPm| JOYNERr| Ys| Yt| JYBPDDAPUu| 7/9/2026;

Computational aspects of social networks / Weili Wu, Zhao Zhang, Ding-Zhu Du.

Author/creator	Wu, Weili author.
Other author	Zhang, Zhao (Computer scientist), author.
Other author	Du, Dingzhu, author.
Format	Book
Publication	Cham : Springer, 2026.
Description	409 pages : illustrations (black and white, and colour) ; 24 cm.
Subjects	Online social networks--Data processing.

Series	Springer optimization and its applications ;
Contents	Introduction and community -- Social influence -- Monte Carlo method -- Viral marketing -- Nonsubmodular optimization -- Local optimum -- Rumor -- Competitive influence -- Adaptive influence -- Robust and machine learning -- Deep learning in social networks.
Abstract	This book offers a comprehensive exploration of computational social networks, focusing on the algorithmic and optimization aspects crucial for applications across diverse domains. As social data proliferates through platforms like Facebook, LinkedIn, and Skype, the need for efficient techniques to extract meaningful insights becomes paramount. This volume provides readers with a robust foundation in computational methods tailored for social networks. Key concepts include combinatorial optimization, machine learning applications, and advanced computational techniques. The chapters are meticulously organized to guide readers through fundamental knowledge, optimization strategies, and cutting-edge topics in the field. By integrating lecture notes and selected materials from leading IEEE/ACM publications, this book serves as an essential resource for understanding the complexities of social data analysis. Designed for graduate and senior undergraduate students in computer science and applied mathematics, this book assumes a foundational knowledge of programming and algorithm design. It is an invaluable tool for those seeking to harness the power of computational social networks in fields such as public safety, viral marketing, and misinformation clarification.
ISBN	9783032148322
ISBN	3032148324

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