<div class="csl-bib-body">
<div class="csl-entry">Welke, P., Thiessen, M., & Gärtner, T. (2022, November 30). <i>Expectation Complete Graph Representations Using Graph Homomorphisms</i> [Poster Presentation]. First Learning on Graphs Conference (LoG 2022), Unknown. https://doi.org/10.34726/3883</div>
</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/175918
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dc.identifier.uri
https://doi.org/10.34726/3883
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dc.description.abstract
We propose and study a practical graph embedding that *in expectation* is able to distinguish all non-isomorphic graphs and can be computed in polynomial time. The embedding is based on Lovász' characterisation of graph isomorphism through an infinite dimensional vector of homomorphism counts. Recent work has studied the expressiveness of graph embeddings by comparing their ability to distinguish graphs to that of the Weisfeiler-Leman hierarchy. While previous methods have either limited expressiveness or are computationally impractical, we devise efficient sampling-based alternatives that are maximally expressive in expectation. We empirically evaluate our proposed embeddings and show competitive results on several benchmark graph learning tasks.
en
dc.language.iso
en
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dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
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dc.subject
Machine Learning
en
dc.title
Expectation Complete Graph Representations Using Graph Homomorphisms
en
dc.type
Presentation
en
dc.type
Vortrag
de
dc.rights.license
Creative Commons Namensnennung 4.0 International
de
dc.rights.license
Creative Commons Attribution 4.0 International
en
dc.identifier.doi
10.34726/3883
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dc.contributor.affiliation
University of Bonn, Germany
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dc.type.category
Poster Presentation
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tuw.researchTopic.id
I4a
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tuw.researchTopic.name
Information Systems Engineering
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tuw.researchTopic.value
100
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tuw.linking
https://openreview.net/forum?id=8GJyW4i2oST
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tuw.linking
https://github.com/pwelke/homcount
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tuw.publication.orgunit
E194-06 - Forschungsbereich Machine Learning
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tuw.author.orcid
0000-0002-2123-3781
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tuw.author.orcid
0000-0001-9333-2685
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tuw.author.orcid
0000-0001-5985-9213
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dc.rights.identifier
CC BY 4.0
de
dc.rights.identifier
CC BY 4.0
en
tuw.event.name
First Learning on Graphs Conference (LoG 2022)
en
tuw.event.startdate
09-12-2022
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tuw.event.enddate
12-12-2022
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tuw.event.online
Online
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tuw.event.type
Event for scientific audience
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tuw.event.country
unknown
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tuw.event.presenter
Welke, Pascal
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tuw.event.presenter
Thiessen, Maximilian
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tuw.presentation.online
Online
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wb.sciencebranch
Informatik
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wb.sciencebranch.oefos
1020
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wb.sciencebranch.value
100
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item.languageiso639-1
en
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item.openairetype
conference poster not in proceedings
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item.grantfulltext
open
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item.fulltext
with Fulltext
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item.cerifentitytype
Publications
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item.mimetype
application/pdf
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item.openairecristype
http://purl.org/coar/resource_type/c_18co
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item.openaccessfulltext
Open Access
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crisitem.author.dept
University of Bonn
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crisitem.author.dept
E194-06 - Forschungsbereich Machine Learning
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crisitem.author.dept
E194-06 - Forschungsbereich Machine Learning
-
crisitem.author.orcid
0000-0002-2123-3781
-
crisitem.author.orcid
0000-0001-9333-2685
-
crisitem.author.orcid
0000-0001-5985-9213
-
crisitem.author.parentorg
E194 - Institut für Information Systems Engineering
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crisitem.author.parentorg
E194 - Institut für Information Systems Engineering
