title: Weighting dissimilarities to detect communities in networks.
abstract: Many complex systems can be described as networks exhibiting inner organization as communities of nodes. The identification of communities is a key factor to understand community-based functionality. We propose a family of measures based on the weighted sum of two dissimilarity quantifiers that facilitates efficient classification of communities by tuning the quantifiers' relative weight to the network's particularities. Additionally, two new dissimilarities are introduced and incorporated in our analysis. The effectiveness of our approach is tested by examining the Zachary's Karate Club Network and the Caenorhabditis elegans reactions network. The analysis reveals the method's classification power as confirmed by the efficient detection of intrapathway metabolic functions in C. elegans.
authors.affiliation: Stochastic Dynamics Laboratory, Center for Physics, Venezuelan Institute for Scientific Research, Caracas 1020-A, Venezuela Departamento de Física, FCFM, Universidad de Chile, Santiago, Chile.
authors.forename: Alejandro J
authors.initials: AJ
authors.lastname: Alvarez
authors.affiliation: Stochastic Dynamics Laboratory, Center for Physics, Venezuelan Institute for Scientific Research, Caracas 1020-A, Venezuela.
authors.forename: Carlos E
authors.initials: CE
authors.lastname: Sanz-Rodríguez
authors.affiliation: Stochastic Dynamics Laboratory, Center for Physics, Venezuelan Institute for Scientific Research, Caracas 1020-A, Venezuela juluisca@gmail.com.
authors.forename: Juan Luis
authors.initials: JL
authors.lastname: Cabrera
keywords: classification; community detection; complex network; data mining; metabolic network analysis; social network analysis