Dynamic Latent Space Models for Relational Data

Music City Center 

Latent space models are powerful tools for analyzing relational data, offering low-dimensional representations of interactions. However, many real-world relationships evolve over time, requiring more flexible models. With the increasing availability of dynamic interaction data, capturing these changes is crucial. We extend the latent space model to embed actor trajectories in Euclidean space, enabling better inference of evolving relationships. This framework is particularly useful for studying complex networks, where uncovering latent structures provides critical insights. By tracking how entities' latent positions evolve, we can better understand shifting interaction patterns, emerging structures, and long-term trends, offering valuable perspectives for various domains. This is joint work with Dr. Owen Ward (Simon Fraser University).

network science

latent space models

dynamic networks

spatial embeddings 

Isolated Statisticians