<div class="csl-bib-body">
<div class="csl-entry">Thiessen, M., Welke, P., & Gärtner, T. (2022, October 21). <i>Expectation Complete Graph Representations Using Graph Homomorphisms</i> [Poster Presentation]. New Frontiers in Graph Learning (GLFrontiers) NeurIPS 2022 Workshop, New Orleans, United States of America (the). https://doi.org/10.34726/3863</div>
</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/175801
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dc.identifier.uri
https://doi.org/10.34726/3863
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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' characterization 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
10.34726/3863
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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=Zf-Mn6xzD2B
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tuw.publication.orgunit
E194-06 - Forschungsbereich Machine Learning
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tuw.author.orcid
0000-0001-9333-2685
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tuw.author.orcid
0000-0002-2123-3781
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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
New Frontiers in Graph Learning (GLFrontiers) NeurIPS 2022 Workshop
en
tuw.event.startdate
02-12-2022
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tuw.event.enddate
02-12-2022
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tuw.event.online
Hybrid
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tuw.event.type
Event for scientific audience
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tuw.event.place
New Orleans
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tuw.event.country
US
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tuw.event.presenter
Thiessen, Maximilian
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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.openairecristype
http://purl.org/coar/resource_type/c_18co
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item.mimetype
application/pdf
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item.languageiso639-1
en
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item.openaccessfulltext
Open Access
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item.fulltext
with Fulltext
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item.grantfulltext
open
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item.openairetype
conference poster not in proceedings
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item.cerifentitytype
Publications
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crisitem.author.dept
E194-06 - Forschungsbereich Machine Learning
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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.orcid
0000-0001-9333-2685
-
crisitem.author.orcid
0000-0002-2123-3781
-
crisitem.author.orcid
0000-0001-5985-9213
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crisitem.author.parentorg
E194 - Institut für Information Systems Engineering
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crisitem.author.parentorg
E194 - Institut für Information Systems Engineering
