Topology Inference

Topology inference aims to reconstruct the underlying structure or connectivity of complex systems from observational data, focusing on the relationships between components rather than their individual properties. Current research emphasizes developing robust methods that handle noisy data, time-varying structures, and limited observations, often employing techniques from topological data analysis, machine learning (including neural networks), and signal processing to infer network topologies or the geometry of high-dimensional data. These advancements have significant implications for diverse fields, including network security, structural health monitoring, and the interpretation of complex data sets generated by machine learning models.