GraphSPNs: Sum-Product Networks Benefit From Canonical Orderings

• URL: http://arxiv.org/abs/2408.09451v1
• Date: Sun, 18 Aug 2024 12:19:16 GMT
• Title: GraphSPNs: Sum-Product Networks Benefit From Canonical Orderings
• Authors: Milan Papež, Martin Rektoris, Václav Šmídl, Tomáš Pevný,
• Abstract summary: Graph sum-product networks (GraphSPNs) are a tractable deep generative model which provides exact and efficient inference over (arbitrary parts of) graphs. We demonstrate that GraphSPNs are able to (conditionally) generate novel and chemically valid molecular graphs.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep generative models have recently made a remarkable progress in capturing complex probability distributions over graphs. However, they are intractable and thus unable to answer even the most basic probabilistic inference queries without resorting to approximations. Therefore, we propose graph sum-product networks (GraphSPNs), a tractable deep generative model which provides exact and efficient inference over (arbitrary parts of) graphs. We investigate different principles to make SPNs permutation invariant. We demonstrate that GraphSPNs are able to (conditionally) generate novel and chemically valid molecular graphs, being competitive to, and sometimes even better than, existing intractable models. We find out that (Graph)SPNs benefit from ensuring the permutation invariance via canonical ordering.

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