Community detection in graphs based on surprise maximization using firefly heuristics

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dc.contributor.authorDel Ser, Javier
dc.contributor.authorLobo, Jesus L.
dc.contributor.authorVillar-Rodriguez, Esther
dc.contributor.authorBilbao, Miren Nekane
dc.contributor.authorPerfecto, Cristina
dc.contributor.institutionIA
dc.contributor.institutionQuantum
dc.date.accessioned2024-07-24T11:54:04Z
dc.date.available2024-07-24T11:54:04Z
dc.date.issued2016-11-14
dc.descriptionPublisher Copyright: © 2016 IEEE.
dc.description.abstractThe detection of node clusters (communities) in graphs has been at the core of many modeling paradigms emerging in different fields and disciplines such as Social Sciences, Biology, Chemistry, Telecommunications and Linguistics. When evaluating the quality of a clustering arrangement unsupervised metrics can be utilized (e.g. modularity), which all rely on structural and topological characteristics of the cluster space rather than on an observed ground of truth that should be achieved. One of such metrics is the recently published Surprise, which evaluates how statistically unlikely a given clustering arrangement is with respect to a random network featuring the same distribution of nodes per cluster. To maximize this metric, a number of algorithms have been proposed in the literature, but their comparative performance varies significantly between networks of different shape and size. In this article a novel heuristic community detection approach is proposed as a means to achieve a universally well-performing tool for graph clustering based on Surprise maximization. The heuristic scheme relies on the search procedure of the so-called Firefly Algorithm, a nature-inspired meta-heuristic solver based on the collective behavior, mutual attractiveness and random yet controlled movement of these insects. The proposed technique emulates these observed behavioral patterns of fireflies in the genotype of the graph clustering problem rather than on an encoded representation of its search space (phenotype). Simulation results evince that the performance of our community detection scheme generalizes better than other schemes when applied over synthetically generated graphs with varying properties.en
dc.description.statusPeer reviewed
dc.format.extent7
dc.identifier.citationDel Ser , J , Lobo , J L , Villar-Rodriguez , E , Bilbao , M N & Perfecto , C 2016 , Community detection in graphs based on surprise maximization using firefly heuristics . in 2016 IEEE Congress on Evolutionary Computation, CEC 2016 . , 7744064 , 2016 IEEE Congress on Evolutionary Computation, CEC 2016 , Institute of Electrical and Electronics Engineers Inc. , pp. 2233-2239 , 2016 IEEE Congress on Evolutionary Computation, CEC 2016 , Vancouver , Canada , 24/07/16 . https://doi.org/10.1109/CEC.2016.7744064
dc.identifier.citationconference
dc.identifier.doi10.1109/CEC.2016.7744064
dc.identifier.isbn9781509006229
dc.identifier.urihttps://hdl.handle.net/11556/2363
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85008256611&partnerID=8YFLogxK
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof2016 IEEE Congress on Evolutionary Computation, CEC 2016
dc.relation.ispartofseries2016 IEEE Congress on Evolutionary Computation, CEC 2016
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.subject.keywordsArtificial Intelligence
dc.subject.keywordsModeling and Simulation
dc.subject.keywordsComputer Science Applications
dc.subject.keywordsControl and Optimization
dc.titleCommunity detection in graphs based on surprise maximization using firefly heuristicsen
dc.typeconference output
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