Representation Learning of Dynamic Networks

Music City Center 

This study introduces a novel representation learning model for dynamic networks, capturing evolving relationships within a population. Framing the problem within functional data analysis, we represent dynamic networks as matrix-valued functions and embed them into a lower-dimensional functional space. This space preserves network topology while enabling attribute learning, community detection, and link prediction. Our model accommodates asymmetric embeddings to distinguish nodes' regulatory and receiving roles, ensuring continuity over time. Unlike discrete-time methods, our approach leverages a functional representation to infer network structures at unobserved time points. We validate our model through simulations and real-world applications, demonstrating superior link prediction accuracy compared to existing approaches. Applying our method to dynamic social networks in ant colonies, we uncover meaningful patterns in interactions and role transitions. Our findings align with known ant colony behaviors, highlighting the model's interpretability and utility in analyzing evolving networks. This work provides a statistical framework balancing representation learning capacity with interpretability, offering insights into dynamic network structures.

Functional data analysis

Representation learning

Graph analysis

Dimension reduction

Community detection