Effectiveness of internal evaluation metrics for community detection based on clustering

dc.contributor.authorWasim, Muhammad
dc.contributor.authorUllah, Ubaid
dc.contributor.authorAl-Obeidat, Feras
dc.contributor.authorAmin, Adnan
dc.contributor.authorMoreira, Fernando
dc.date.accessioned2024-04-08T14:57:50Z
dc.date.available2024-04-08T14:57:50Z
dc.date.issued2024-03-18
dc.description.abstractThe exploration of complex networks and the arrangement of communities is a widely researched topic across various fields, reflecting research interest in a multitude of domains. Clustering algorithms have emerged as a prominent tool for community detection, gaining considerable attention in recent decades. To assess the effectiveness of clustering algorithms, various evaluation metrics are employed, including internal, external, and relative metrics. In this paper, the effectiveness of several partitional clustering algorithms is analyzed to identify communities. The algorithms reviewed include graph-based, centroid-based, and modal-based algorithms, which were tested on various datasets. The study’s primary aim is to determine how accurate and reliable internal evaluation metrics are for community detection through clustering. The study’s findings reveal that the k-means algorithm excelled in silhouette score and sum of squared error evaluation, while affinity propagation outperformed others in terms of the davies-bouldin index and adjusted mutual information. These results can provide valuable guidance and support in the domain of community detection, aiding researchers in achieving more accurate and effective analyses of complex network structures.
dc.identifier.citationWasim, M., Ullah, U., Al-Obeidat, F., Amin, A., & Moreira, F. (2024). Effectiveness of internal evaluation metrics for community detection based on clustering. In A. Ullah, S. Anwar, D. Calandra, & R. Di Fuccio (Eds.), Proceedings of International Conference on Information Technology and Applications. ICITA 2022. Lecture Notes in Networks and Systems, (vol. 839, pp. 65-75). Springer. https://doi.org/10.1007/978-981-99-8324-7_7. Repositório Institucional UPT. https://hdl.handle.net/11328/5575
dc.identifier.isbn978-981-99-8323-0
dc.identifier.isbn978-981-99-8324-7
dc.identifier.urihttps://hdl.handle.net/11328/5575
dc.language.isoeng
dc.publisherSpringer
dc.relation.hasversionhttps://doi.org/10.1007/978-981-99-8324-7_7
dc.rightsrestricted access
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectCommunity detection
dc.subjectComplex networks
dc.subjectClustering
dc.subjectUn-directed graph
dc.subjectPartitional clustering
dc.subjectSilhouette score
dc.subjectDavies-bouldin index
dc.subjectSum of square error
dc.subjectAdjusted mutual information
dc.subject.fosCiências Naturais - Ciências da Computação e da Informação
dc.titleEffectiveness of internal evaluation metrics for community detection based on clustering
dc.typeconference paper
dspace.entity.typePublication
oaire.citation.endPage75
oaire.citation.startPage65
oaire.citation.titleProceedings of International Conference on Information Technology and Applications. ICITA 2022. Lecture Notes in Networks and Systems
oaire.citation.volume839
person.affiliation.nameUniversidade Portucalense
person.familyNameMoreira
person.givenNameFernando
person.identifier.ciencia-id7B1C-3A29-9861
person.identifier.orcid0000-0002-0816-1445
person.identifier.ridP-9673-2016
person.identifier.scopus-author-id8649758400
relation.isAuthorOfPublicationbad3408c-ee33-431e-b9a6-cb778048975e
relation.isAuthorOfPublication.latestForDiscoverybad3408c-ee33-431e-b9a6-cb778048975e

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