Latent dIstance Model

Latent distance models represent data points as locations in a low-dimensional latent space, where distances between points reflect their relationships. Research focuses on improving these models for various applications, including network analysis (e.g., link prediction, community detection) and molecular property prediction, often employing graph neural networks and incorporating techniques like survival processes to model dynamic relationships and signed distances to capture positive and negative interactions. These advancements enhance the accuracy and interpretability of analyses across diverse fields, leading to improved understanding of complex systems and more reliable predictions.