<div class="csl-bib-body">
<div class="csl-entry">Welke, P., Thiessen, M., Jogl, F., & Gärtner, T. (2023). Expectation-Complete Graph Representations with Homomorphisms. In A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato, & J. Scarlett (Eds.), <i>Proceedings of the 40th International Conference on Machine Learning</i> (pp. 36910–36925). Proceedings of Machine Learning Research.</div>
</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/188939
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dc.description.abstract
We investigate novel random graph embeddings that can be computed in expected polynomial time and that are able to distinguish all non-isomorphic graphs in expectation. Previous graph embeddings have limited expressiveness and either cannot distinguish all graphs or cannot be computed efficiently for every graph. To be able to approximate arbitrary functions on graphs, we are interested in efficient alternatives that become arbitrarily expressive with increasing resources. Our approach is based on Lovász’ characterisation of graph isomorphism through an infinite dimensional vector of homomorphism counts. Our empirical evaluation shows competitive results on several benchmark graph learning tasks.
en
dc.description.sponsorship
WWTF Wiener Wissenschafts-, Forschu und Technologiefonds
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dc.language.iso
en
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dc.subject
graph representation
en
dc.subject
graph homomorphism
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dc.subject
expressivity
en
dc.title
Expectation-Complete Graph Representations with Homomorphisms
en
dc.type
Inproceedings
en
dc.type
Konferenzbeitrag
de
dc.description.startpage
36910
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dc.description.endpage
36925
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dc.relation.grantno
ICT22-059
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dcterms.dateSubmitted
2023-08
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dc.type.category
Full-Paper Contribution
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dc.relation.eissn
2640-3498
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tuw.booktitle
Proceedings of the 40th International Conference on Machine Learning
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tuw.container.volume
202
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tuw.peerreviewed
true
-
tuw.book.ispartofseries
Proceedings of Machine Learning Research
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tuw.relation.publisher
Proceedings of Machine Learning Research
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tuw.project.title
Structured Data Learning with Generalized Similarities
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tuw.researchTopic.id
I4
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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://proceedings.mlr.press/v202/welke23a.html
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tuw.linking
https://proceedings.mlr.press/v202/
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tuw.publication.orgunit
E194-06 - Forschungsbereich Machine Learning
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dc.description.numberOfPages
16
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tuw.author.orcid
0000-0001-9333-2685
-
tuw.author.orcid
0000-0001-5985-9213
-
tuw.editor.orcid
0000-0002-3971-7127
-
tuw.editor.orcid
0000-0002-6139-7334
-
tuw.editor.orcid
0000-0002-7975-0044
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tuw.event.name
Fortieth International Conference on Machine Learning (ICML 2023)
en
tuw.event.startdate
23-07-2023
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tuw.event.enddate
29-07-2023
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tuw.event.online
On Site
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tuw.event.type
Event for scientific audience
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tuw.event.place
Honolulu
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tuw.event.country
US
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tuw.event.presenter
Welke, Pascal
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tuw.event.presenter
Jogl, Fabian
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tuw.event.presenter
Gärtner, Thomas
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tuw.event.track
Multi Track
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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.openairetype
Inproceedings
-
item.openairetype
Konferenzbeitrag
-
item.cerifentitytype
Publications
-
item.cerifentitytype
Publications
-
item.languageiso639-1
en
-
item.grantfulltext
none
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item.openairecristype
http://purl.org/coar/resource_type/c_18cf
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item.openairecristype
http://purl.org/coar/resource_type/c_18cf
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item.fulltext
no Fulltext
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crisitem.author.dept
E194-06 - Forschungsbereich Machine Learning
-
crisitem.author.dept
E194-06 - Forschungsbereich Machine Learning
-
crisitem.author.dept
E192-02 - Forschungsbereich Databases and Artificial Intelligence
-
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
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crisitem.author.parentorg
E192 - Institut für Logic and Computation
-
crisitem.author.parentorg
E194 - Institut für Information Systems Engineering
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crisitem.project.funder
WWTF Wiener Wissenschafts-, Forschu und Technologiefonds