Beyond Graph Neural Networks with Lifted Relational Neural Networks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F23%3A00361157" target="_blank" >RIV/68407700:21230/23:00361157 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.48448/4eps-hs54" target="_blank" >https://doi.org/10.48448/4eps-hs54</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.48448/4eps-hs54" target="_blank" >10.48448/4eps-hs54</a>
Alternative languages
Result language
angličtina
Original language name
Beyond Graph Neural Networks with Lifted Relational Neural Networks
Original language description
We introduce a declarative differentiable programming framework, based on the language of Lifted Relational Neural Networks, where small parameterized logic programs are used to encode deep relational learning scenarios through the underlying symmetries. When presented with relational data, such as various forms of graphs, the logic program interpreter dynamically unfolds differentiable computational graphs to be used for the program parameter optimization by standard means. Following from the declarative, relational logic-based encoding, this results into a unified representation of a wide range of neural models in the form of compact and elegant learning programs, in contrast to the existing procedural approaches operating directly on the computational graph level. We illustrate how this idea can be used for a concise encoding of existing advanced neural architectures, with the main focus on Graph Neural Networks (GNNs). Importantly, using the framework, we also show how the contemporary GNN models can be easily extended towards higher expressiveness in various ways. In the experiments, we demonstrate correctness and computation efficiency through comparison against specialized GNN frameworks, while shedding some light on the learning performance of the existing GNN models.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
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)
Result continuities
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)
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 37th AAAI Conference on Artificial Intelligence
ISBN
978-1-57735-880-0
ISSN
2159-5399
e-ISSN
2374-3468
Number of pages
44
Pages from-to
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Publisher name
AAAI Press
Place of publication
Menlo Park
Event location
Washington, DC
Event date
Feb 7, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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