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.

翻译：复杂交互系统（包括大脑活动、金融价格波动和物理集体现象）的动态行为与系统各组成部分之间的底层相互作用相关联。利用可观测动力学揭示此类系统中相互作用关系的问题被称为关系推断。本研究受概率时间序列插补的自监督方法启发，提出了用于关系推断的扩散模型（DiffRI）。DiffRI通过学习条件扩散建模，推断各组件间连接存在的概率。