GraphSPNs: Sum-Product Networks Benefit From Canonical Orderings

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F24%3A00382074" target="_blank" >RIV/68407700:21230/24:00382074 - isvavai.cz</a>

Result on the web

<a href="https://openreview.net/forum?id=NKdl7FHlFK" target="_blank" >https://openreview.net/forum?id=NKdl7FHlFK</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

GraphSPNs: Sum-Product Networks Benefit From Canonical Orderings

Original language description

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.

Czech name

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Czech description

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Type

O - Miscellaneous

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GA22-32620S" target="_blank" >GA22-32620S: Unsupervised learning from heterogenous structured data</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů