Lifted Relational Neural Networks: from Graphs to Deep Relational Learning

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F23%3A00361162" target="_blank" >RIV/68407700:21230/23:00361162 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.3233/FAIA230147" target="_blank" >https://doi.org/10.3233/FAIA230147</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3233/FAIA230147" target="_blank" >10.3233/FAIA230147</a>

Result language
angličtina
Original language name
Lifted Relational Neural Networks: from Graphs to Deep Relational Learning
Original language description
Lifted Relational Neural Networks (LRNNs) were introduced in 2015 as a framework for combining logic programming with neural networks for efficient learning of latent relational structures, such as various subgraph patterns in molecules. In this chapter, we will briefly re-introduce the framework and explain its current relevance in the context of contemporary Graph Neural Networks (GNNs). Particularly, we will detail how the declarative nature of differentiable logic programming in LRNNs can be used to elegantly capture various GNN variants and generalize to novel, even more expressive, deep relational learning concepts. Additionally, we will briefly demonstrate practical use and computation performance of the framework.
Czech name
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Czech description
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Type
C - Chapter in a specialist book
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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