The art of community detection

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dc.creatorGulbahce, Natali
dc.creatorLehmann, Sune
dc.date2008-07-11
dc.date.accessioned2026-07-07T09:49:50Z
dc.date.available2026-07-07T09:49:50Z
dc.descriptionNetworks in nature possess a remarkable amount of structure. Via a series of data-driven discoveries, the cutting edge of network science has recently progressed from positing that the random graphs of mathematical graph theory might accurately describe real networks to the current viewpoint that networks in nature are highly complex and structured entities. The identification of high order structures in networks unveils insights into their functional organization. Recently, Clauset, Moore, and Newman, introduced a new algorithm that identifies such heterogeneities in complex networks by utilizing the hierarchy that necessarily organizes the many levels of structure. Here, we anchor their algorithm in a general community detection framework and discuss the future of community detection.
dc.description10 pages, 2 figures. to appear in Bioessays
dc.identifierhttps://arxiv.org/abs/0807.1833
dc.identifierhttp://arxiv.org/abs/0807.1833
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/164737
dc.subjectPhysics and Society
dc.subjectStatistical Mechanics
dc.subjectData Analysis, Statistics and Probability
dc.subjectQuantitative Methods
dc.titleThe art of community detection
dc.typetext

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