Multi Layer Network

Multi-layer networks represent complex systems as interconnected layers, enabling analysis of diverse relationships and dependencies within and between layers. Current research focuses on developing efficient algorithms for tasks such as community detection and classification within these structures, employing techniques like graph convolutional networks and tailored optimization methods for specific applications (e.g., Stackelberg games for dynamic resource allocation). These advancements are proving valuable in diverse fields, including improving the accuracy of clinical phenotyping, optimizing federated learning across distributed systems, and enhancing solutions for complex problems like crime interdiction. The ability to effectively model and analyze multi-layer networks is driving progress in understanding and managing intricate systems across various scientific disciplines.