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<div data-olk-copy-source="MessageBody">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.</div><div>&#xa0;</div><div>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.</div><div>&#xa0;</div><div>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.</div>

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Computational Aspects of Social Networks

  • 教材

  • 发表日期：

    2026年

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  • Weili Wu,
  • Zhao Zhang,
  • Ding-Zhu Du

摘要

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.