Visibility Graph

Visibility graphs represent data as networks where nodes are data points and edges connect points with a clear line of sight, enabling efficient analysis of spatial relationships and patterns. Current research focuses on improving the efficiency and adaptability of visibility graph construction and pathfinding algorithms, particularly within complex or dynamic environments, and integrating them with machine learning models like graph convolutional networks for tasks such as navigation, classification, and feature extraction. These advancements are impacting diverse fields, including robotics, medical diagnostics (e.g., arrhythmia classification, sleep staging), and environmental monitoring (e.g., fire escape route planning), by providing efficient and robust solutions for complex problems.