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.

Offers a comprehensive review of the rapidly expanding field of algebraic and geometric methods in network analysis Presents in-depth discussions on network growth Explores a vast array of special topics and applications illustrating the wide use of network models

Inhalt

Noisy Binary Choice(Ising) Games.- Uncertainty of Market Network Analysis.- When Do Pivotal Nodes Emerge? Tail Behavior and Bipartite Imbalance in Quota-Constrained Flow Networks.- Geometric Machine Learning via Ricci Flow: Unveiling Hidden Structures in Complex Datasets.- Rewire or Reward: Linear Externalities and the Social Planner's Problems in Networks.- AI-Assisted Tools in Bibliometric Network Analysis.- Compartmental Model Approach to Drug Offense Mitigation.- Uncovering Network Patterns in the Russian Labor Market for Data Analysts Through Multimodal Graphs.- Application of Multiple Methods to Explore Thematic Evolution of Wellbeing Studies.