Beyond Weisfeiler–Lehman with local ego-network encodings

Identifying similar network structures is key to capturing graph isomorphisms and learning representations that exploit structural information encoded in graph data. This work shows that ego networks can produce a structural encoding scheme for arbitrary graphs with greater expressivity than the Wei...

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Autores: Alvarez-Gonzalez, Nurudin, Kaltenbrunner, Andreas, Gómez, Vicenç
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2023
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10230/71827
Acceso en línea:http://hdl.handle.net/10230/71827
http://dx.doi.org/10.3390/make5040063
Access Level:acceso abierto
Palabra clave:Graph neural networks
Graph representation learning
Weisfeiler–Lehman
Graph isomorphism
GNN expressivity
Ego networks
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spelling Beyond Weisfeiler–Lehman with local ego-network encodingsAlvarez-Gonzalez, NurudinKaltenbrunner, AndreasGómez, VicençGraph neural networksGraph representation learningWeisfeiler–LehmanGraph isomorphismGNN expressivityEgo networksIdentifying similar network structures is key to capturing graph isomorphisms and learning representations that exploit structural information encoded in graph data. This work shows that ego networks can produce a structural encoding scheme for arbitrary graphs with greater expressivity than the Weisfeiler–Lehman (1-WL) test. We introduce IGEL, a preprocessing step to produce features that augment node representations by encoding ego networks into sparse vectors that enrich message passing (MP) graph neural networks (GNNs) beyond 1-WL expressivity. We formally describe the relation between IGEL and 1-WL, and characterize its expressive power and limitations. Experiments show that IGEL matches the empirical expressivity of state-of-the-art methods on isomorphism detection while improving performance on nine GNN architectures and six graph machine learning tasks.This work has been co-funded by MCIN/AEI/10.13039/501100011033 under the Maria de Maeztu Units of Excellence Programme (CEX2021-001195-M). This publication is part of the action CNS2022-136178 financed by MCIN/AEI/10.13039/501100011033 and by the European Union Next Generation EU/PRTR.MDPI202520252023info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/71827http://dx.doi.org/10.3390/make5040063http://hdl.handle.net/10230/71827reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésMachine Learning and Knowledge Extraction. 2023 Sep 22;5(4):1234-65info:eu-repo/grantAgreement/ES/3PE/CNS2022-136178© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:10230/718272026-05-29T05:05:01Z
dc.title.none.fl_str_mv Beyond Weisfeiler–Lehman with local ego-network encodings
title Beyond Weisfeiler–Lehman with local ego-network encodings
spellingShingle Beyond Weisfeiler–Lehman with local ego-network encodings
Alvarez-Gonzalez, Nurudin
Graph neural networks
Graph representation learning
Weisfeiler–Lehman
Graph isomorphism
GNN expressivity
Ego networks
title_short Beyond Weisfeiler–Lehman with local ego-network encodings
title_full Beyond Weisfeiler–Lehman with local ego-network encodings
title_fullStr Beyond Weisfeiler–Lehman with local ego-network encodings
title_full_unstemmed Beyond Weisfeiler–Lehman with local ego-network encodings
title_sort Beyond Weisfeiler–Lehman with local ego-network encodings
dc.creator.none.fl_str_mv Alvarez-Gonzalez, Nurudin
Kaltenbrunner, Andreas
Gómez, Vicenç
author Alvarez-Gonzalez, Nurudin
author_facet Alvarez-Gonzalez, Nurudin
Kaltenbrunner, Andreas
Gómez, Vicenç
author_role author
author2 Kaltenbrunner, Andreas
Gómez, Vicenç
author2_role author
author
dc.subject.none.fl_str_mv Graph neural networks
Graph representation learning
Weisfeiler–Lehman
Graph isomorphism
GNN expressivity
Ego networks
topic Graph neural networks
Graph representation learning
Weisfeiler–Lehman
Graph isomorphism
GNN expressivity
Ego networks
description Identifying similar network structures is key to capturing graph isomorphisms and learning representations that exploit structural information encoded in graph data. This work shows that ego networks can produce a structural encoding scheme for arbitrary graphs with greater expressivity than the Weisfeiler–Lehman (1-WL) test. We introduce IGEL, a preprocessing step to produce features that augment node representations by encoding ego networks into sparse vectors that enrich message passing (MP) graph neural networks (GNNs) beyond 1-WL expressivity. We formally describe the relation between IGEL and 1-WL, and characterize its expressive power and limitations. Experiments show that IGEL matches the empirical expressivity of state-of-the-art methods on isomorphism detection while improving performance on nine GNN architectures and six graph machine learning tasks.
publishDate 2023
dc.date.none.fl_str_mv 2023
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10230/71827
http://dx.doi.org/10.3390/make5040063
http://hdl.handle.net/10230/71827
url http://hdl.handle.net/10230/71827
http://dx.doi.org/10.3390/make5040063
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Machine Learning and Knowledge Extraction. 2023 Sep 22;5(4):1234-65
info:eu-repo/grantAgreement/ES/3PE/CNS2022-136178
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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