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dc.contributor.authorRodríguez Corominas, Guillem
dc.contributor.authorBlesa Aguilera, Maria Josep
dc.contributor.authorBlum, Christian
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.date.accessioned2022-12-15T10:05:23Z
dc.date.available2022-12-15T10:05:23Z
dc.date.issued2023-01
dc.identifier.citationRodríguez, G.; Blesa, M.; Blum, C. AntNetAlign: Ant colony optimization for network alignment. "Applied soft computing", 2023, vol. 132, article 109832, p. 1-19.
dc.identifier.issn1568-4946
dc.identifier.urihttp://hdl.handle.net/2117/378213
dc.descriptionThe code (and data) in this article has been certified as Reproducible by Code Ocean: (https://codeocean.com/). More information on the Reproducibility Badge Initiative is available at https://www.elsevier.com/physical-sciences-andengineering/computer-science/journals
dc.description.abstractNetwork Alignment (NA) is a hard optimization problem with important applications such as, for example, the identification of orthologous relationships between different proteins and of phylogenetic relationships between species. Given two (or more) networks, the goal is to find an alignment between them, that is, a mapping between their respective nodes such that the topological and functional structure is well preserved. Although the problem has received great interest in recent years, there is still a need to unify the different trends that have emerged from diverse research areas. In this paper, we introduce AntNetAlign, an Ant Colony Optimization (ACO) approach for solving the problem. The proposed approach makes use of similarity information extracted from the input networks to guide the construction process. Combined with an improvement measure that depends on the current construction state, it is able to optimize any of the three main topological quality measures. We provide an extensive experimental evaluation using real-world instances that range from Protein–Protein Interaction (PPI) networks to Social Networks. Results show that our method outperforms other state-of-the-art approaches in two out of three of the tested scores within a reasonable amount of time, specially in the important score. Moreover, it is able to obtain near-optimal results when aligning networks with themselves. Furthermore, in larger instances, our algorithm was still able to compete with the best performing method in this regard.
dc.description.sponsorshipChristian Blum and Guillem Rodríguez Corominas, Spain were supported by grants PID2019-104156GB-I00 and TED2021- 129319B-I00 funded by MCIN/AEI/10.13039/501100011033. Maria J. Blesa acknowledges support from AEI, Spain under grant PID2020-112581GB-C21 (MOTION) and the Catalan Agency for Management of University and Research Grants (AGAUR), Spain under grant 2017-SGR-786 (ALBCOM).
dc.format.extent19 p.
dc.language.isoeng
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectÀrees temàtiques de la UPC::Informàtica::Informàtica teòrica::Algorísmica i teoria de la complexitat
dc.subject.lcshGraph theory
dc.subject.lcshCombinatorial optimization
dc.subject.lcshAlgorithms
dc.subject.otherNetwork alignment
dc.subject.otherAnt colony optimization
dc.titleAntNetAlign: Ant colony optimization for network alignment
dc.typeArticle
dc.subject.lemacGrafs, Teoria de
dc.subject.lemacOptimització combinatòria
dc.subject.lemacAlgorismes
dc.contributor.groupUniversitat Politècnica de Catalunya. ALBCOM - Algorísmia, Bioinformàtica, Complexitat i Mètodes Formals
dc.identifier.doi10.1016/j.asoc.2022.109832
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S156849462200881X
dc.rights.accessOpen Access
local.identifier.drac34943665
dc.description.versionPostprint (published version)
dc.relation.projectidinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-112581GB-C21/ES/MODELOS Y TECNICAS PARA EL PROCESAMIENTO DE INFORMACION A GRAN ESCALA -- BARCELONA/
local.citation.authorRodríguez, G.; Blesa, M.; Blum, C.
local.citation.publicationNameApplied soft computing
local.citation.volume132
local.citation.numberarticle 109832
local.citation.startingPage1
local.citation.endingPage19


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