A novel approach to graph distinction through GENEOs and permutants

Abstract The theory of Group Equivariant Non-Expansive Operators (GENEOs) was initially developed in Topological Data Analysis for the geometric approximation of data observers, including their invariances and symmetries. This paper departs from that line of research and explores the use of GENEOs f...

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Main Authors: Giovanni Bocchi, Massimo Ferri, Patrizio Frosini
Format: Article
Language:English
Published: Nature Portfolio 2025-02-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-025-90152-7
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author Giovanni Bocchi
Massimo Ferri
Patrizio Frosini
author_facet Giovanni Bocchi
Massimo Ferri
Patrizio Frosini
author_sort Giovanni Bocchi
collection DOAJ
description Abstract The theory of Group Equivariant Non-Expansive Operators (GENEOs) was initially developed in Topological Data Analysis for the geometric approximation of data observers, including their invariances and symmetries. This paper departs from that line of research and explores the use of GENEOs for distinguishing r-regular graphs up to isomorphisms. In doing so, we aim to test the capabilities and flexibility of these operators. Our experiments show that GENEOs offer a good compromise between efficiency and computational cost in comparing r-regular graphs, while their actions on data are easily interpretable. This supports the idea that GENEOs could be a general-purpose approach to discriminative problems in Machine Learning when some structural information about data and observers is explicitly given.
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spelling doaj-art-c68ca7e3d01a45dda38032ca2b36a2b52025-08-20T02:15:11ZengNature PortfolioScientific Reports2045-23222025-02-0115111110.1038/s41598-025-90152-7A novel approach to graph distinction through GENEOs and permutantsGiovanni Bocchi0Massimo Ferri1Patrizio Frosini2Department of Environmental Science and Policy, University of MilanDepartment of Mathematics and ARCES, University of BolognaDepartment of Computer Science, University of PisaAbstract The theory of Group Equivariant Non-Expansive Operators (GENEOs) was initially developed in Topological Data Analysis for the geometric approximation of data observers, including their invariances and symmetries. This paper departs from that line of research and explores the use of GENEOs for distinguishing r-regular graphs up to isomorphisms. In doing so, we aim to test the capabilities and flexibility of these operators. Our experiments show that GENEOs offer a good compromise between efficiency and computational cost in comparing r-regular graphs, while their actions on data are easily interpretable. This supports the idea that GENEOs could be a general-purpose approach to discriminative problems in Machine Learning when some structural information about data and observers is explicitly given.https://doi.org/10.1038/s41598-025-90152-7
spellingShingle Giovanni Bocchi
Massimo Ferri
Patrizio Frosini
A novel approach to graph distinction through GENEOs and permutants
Scientific Reports
title A novel approach to graph distinction through GENEOs and permutants
title_full A novel approach to graph distinction through GENEOs and permutants
title_fullStr A novel approach to graph distinction through GENEOs and permutants
title_full_unstemmed A novel approach to graph distinction through GENEOs and permutants
title_short A novel approach to graph distinction through GENEOs and permutants
title_sort novel approach to graph distinction through geneos and permutants
url https://doi.org/10.1038/s41598-025-90152-7
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