Diffusion model for relational inference
This work addresses relational inference for complex systems like brain activities and financial markets, but appears incremental as it adapts an existing self-supervised method to this task.
The authors tackled the problem of inferring interaction relations in complex systems from observable dynamics, proposing a diffusion model for relational inference (DiffRI) that learns connection probabilities through conditional diffusion modeling.
Dynamical behaviors of complex interacting systems, including brain activities, financial price movements, and physical collective phenomena, are associated with underlying interactions between the system's components. The issue of uncovering interaction relations in such systems using observable dynamics is called relational inference. In this study, we propose a Diffusion model for Relational Inference (DiffRI), inspired by a self-supervised method for probabilistic time series imputation. DiffRI learns to infer the probability of the presence of connections between components through conditional diffusion modeling.