Unknown Network

Research on "unknown networks" focuses on analyzing and manipulating systems where the underlying connections or topology are not fully known, aiming to achieve tasks like resource allocation, community detection, or signal processing despite this lack of complete information. Current approaches leverage machine learning, particularly graph neural networks and reinforcement learning, along with novel network architectures designed for efficiency and scalability, to infer network structure, optimize performance, and control spreading processes. This research is significant for its applications in diverse fields, including social network analysis, epidemic control, and efficient deep learning training, where complete network knowledge is often unavailable or impractical to obtain.