The book tackles two core challenges in modern network analysis: developing robust models to capture complex network dynamics and designing efficient algorithms for optimization problems. Part I delves into network models, presenting foundational analyses such as noisy binary choice games and innovative frameworks like geometric machine learning via Ricci flow. Part II shifts to optimization in networks and applications, featuring practical algorithms and real-world implementations, including AI-assisted tools in bibliometric network analysis and a compartmental model approaches.

Ideal for researchers, graduate students, and practitioners in network science, operations research, and applied mathematics, this book provides both theoretical insights and practical applications. With contributions from leading experts, it offers a rich resource for those seeking to understand and leverage the power of network analysis in diverse fields.