dc.creatorHarenberg, Steve
dc.creatorBello, Gonzalo
dc.creatorGjeltema, L.
dc.creatorRanshous, Stephen
dc.creatorHarlalka, Jitendra
dc.creatorSeay, Ramona
dc.creatorPadmanabhan, Kanchana
dc.creatorSamatova, Nagiza
dc.date2016-09-21T18:24:47Z
dc.date2016-09-21T18:24:47Z
dc.date2014
dc.date.accessioned2023-09-26T22:18:44Z
dc.date.available2023-09-26T22:18:44Z
dc.identifierHARENBERG, Steve; et al. Community detection in large-scale networks: a survey and empirical evaluation. WIREs Comput Stat., v.6, n.6, p.426–439, 2014.
dc.identifierhttps://www.arca.fiocruz.br/handle/icict/15895
dc.identifier10.1002/wics.1319
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8877683
dc.descriptionCommunity detection is a common problem in graph data analytics that consists of finding groups of densely connected nodes with few connections to nodes outside of the group. In particular, identifying communities in large-scale networks is an important task in many scientific domains. In this review, we evaluated eight state-of-the-art and five traditional algorithms for overlapping and disjoint community detection on large-scale real-world networks with known ground-truth communities. These 13 algorithms were empirically compared using goodness metrics that measure the structural properties of the identified communities, as well as performance metrics that evaluate these communities against the ground-truth. Our results show that these two types of metrics are not equivalent. That is, an algorithm may perform well in terms of goodness metrics, but poorly in terms of performance metrics, or vice versa.
dc.formatapplication/pdf
dc.languageeng
dc.publisherWiley Online Library
dc.rightsopen access
dc.subjectAgrupamento
dc.subjectDetecção de comunidades
dc.subjectAvaliação empírica
dc.subjectGráficos
dc.subjectRedes
dc.subjectClustering
dc.subjectCommunity detection
dc.subjectEmpirical evaluation
dc.subjectGraphs
dc.subjectGround-truth
dc.subjectNetworks
dc.titleCommunity detection in large-scale networks: a survey and empirical evaluation
dc.typeArticle


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