Network Topology Inference

Network topology inference aims to reconstruct the structure of networks from observational data, focusing on identifying connections between nodes. Current research emphasizes efficient algorithms for dynamic networks, including online learning methods and those incorporating sparsity constraints, often leveraging Gaussian graphical models or graphon models for improved scalability and robustness. These advancements are crucial for applications ranging from analyzing financial markets and disease spread to optimizing communication in decentralized machine learning and improving multi-robot coordination, where accurate network topology prediction is essential for effective control.