Interdependent Network

Interdependent networks research focuses on understanding and modeling systems where components are interconnected and influence each other's behavior, impacting overall system performance and resilience. Current research emphasizes developing methods to analyze these complex interactions, including graph neural networks, Bayesian inference, and block-structured optimization algorithms, to detect vulnerabilities, predict cascading failures, and optimize system design. This work has significant implications for diverse fields, from improving high-performance computing efficiency and designing robust urban infrastructure to enhancing human-robot trust and understanding the dynamics of complex systems like air traffic management. The ultimate goal is to develop tools and frameworks for designing more resilient and efficient interconnected systems.