Network Reconfiguration

Network reconfiguration focuses on optimizing the structure of interconnected systems by dynamically altering their topology to achieve specific objectives, such as improved communication, enhanced power grid efficiency, or increased cybersecurity. Current research employs diverse approaches, including graph neural networks and reinforcement learning algorithms like Deep Q-Networks, to efficiently explore the vast space of possible reconfigurations and learn optimal strategies. These advancements have significant implications for various fields, enabling improved performance in areas such as multi-agent systems, smart grids, and network security through the development of more robust and adaptable network designs.