A dual assortative measure of community structure

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dc.creatorKaplan, Todd D.
dc.creatorForrest, Stephanie
dc.date2008-01-21
dc.date.accessioned2026-07-07T08:55:47Z
dc.date.available2026-07-07T08:55:47Z
dc.descriptionCurrent community detection algorithms operate by optimizing a statistic called modularity, which analyzes the distribution of positively weighted edges in a network. Modularity does not account for negatively weighted edges. This paper introduces a dual assortative modularity measure (DAMM) that incorporates both positively and negatively weighted edges. We describe the the DAMM statistic and illustrate its utility in a community detection algorithm. We evaluate the efficacy of the algorithm on both computer generated and real-world networks, showing that DAMM broadens the domain of networks that can be analyzed by community detection algorithms.
dc.identifierhttps://arxiv.org/abs/0801.3290
dc.identifierhttp://arxiv.org/abs/0801.3290
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/146384
dc.subjectData Analysis, Statistics and Probability
dc.subjectPhysics and Society
dc.titleA dual assortative measure of community structure
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