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.

Provides the fundamentals of computational social networks Discusses combinatorial optimization problems raised from computational social networks Explores current active research topics in computational social networks

Autorentext

Weili Wu is an IEEE-fellow and a full professor in the Department of Computer Science, The University of Texas at Dallas, Richardson, TX, USA. She received the MS and PhD degrees from the Department of Computer Science, University of Minnesota, Minneapolis, MN, USA, in 1998 and 2002, respectively. She has published a large number of research articles in the general area of data communication and data management, including the design and analysis of algorithms for optimization problems that occur in wireless networking environments and various data systems. Zhang Zhao is a Distinguished Professor at Zhejiang Normal University. Her primary research focuses on the design and analysis of combinatorial optimization algorithms. She has led and completed four projects funded by NSFC (including the Fund for Excellent Young Scholars) and four projects funded by the Ministry of Education (including the New Century Excellent Talents Support Program). She has also won the First Prize in Scientific and Technological Progress of Xinjiang. Professor Zhang is a member of the 8th Mathematics Subject Evaluation Group of the Academic Degrees Committee of the State Council, an executive director of the Operations Research Society of China, an associate editor of Journal of Combinatorial Optimization, etc. Ding-Zhu Du is a professor at Department of Computer Science, the University of Texas at Dallas. He received his Master’s degree in 1982 from the Chinese Academy of Sciences and Ph.D. in 1985 from the University of California at Santa Barbara. His research areas include optimization theory and mathematical foundation of computer science. He had worked in MSRI (Berkeley), MIT, Chinese Academy of Sciences, Princeton University, University of Minnesota, and NSF of USA. He was granted the Natural Science First-Class Prize of the Chinese Academy of Sciences in 1992, the National Natural Science Second-Class Prize of China in 1993, the CSTS award of INFORMS in 1998, and the Constantin Caratheodory Prize in 2025.

Inhalt

Introduction and Community.- Social Influence.- Monte Carlo Method.- Viral Marketing.- Nonsubmodular Optimization.- Local Optimum.- Rumor.- Competitive Influence.- Adaptive Influence.- Robust and Machine Learning.