From Uniform to Learned Graph Priors: Diffusion for Structure Discovery

arXiv:2606.11831v1 Announce Type: new Abstract: Neural relational inference (NRI) methods discover interaction graphs from trajectories through variational reasoning on discrete potential edges. However, these methods typically rely on oversimplified, factorized graph priors. Such priors, typically nearing uniform distributions, treat edges as independent entities. This systemic misalignment does not match the real-world systems and yields diffuse and indecisive edge posteriors limiting the reliability of structural discovery. To address this, we propose \textit{Diff-prior}, a diffusion-parame

Source: arXiv cs.LG — read the full report at the original publisher.

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