Diffusion model for relational inference
Shuhan Zheng Ziqiang Li Kantaro Fujiwara Gouhei Tanaka
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. Experiments on both simulated and quasi-real
datasets show that DiffRI is highly competent compared with other
state-of-the-art models in discovering ground truth interactions in an
unsupervised manner. Our code will be made public soon.