19,793 research outputs found
Community detection by consensus genetic-based algorithm for directed networks
Finding communities in networks is a commonly used form of network analysis. There is a myriad of community detection algorithms in the literature to perform this task. In spite of that, the number of community detection algorithms in directed networks is much lower than in undirected networks. However, evaluation measures to estimate the quality of communities in undirected networks nowadays have its adaptation to directed networks as, for example, the well-known modularity measure. This paper introduces a genetic-based consensus clustering to detect communities in directed networks with the directed modularity as the fitness function. Consensus strategies involve combining computational models to improve the quality of solutions generated by a single model. The reason behind the development of a consensus strategy relies on the fact that recent studies indicate that the modularity may fail in detecting expected clusterings. Computational experiments with artificial LFR networks show that the proposed method was very competitive in comparison to existing strategies in the literature. (C) 2016 The Authors. Published by Elsevier B.V.Instituto de Ciência e Tecnologia, Universidade Federal de São Paulo (UNIFESP) Av. Cesare M. G. Lattes, 1201, Eugênio de Mello, São José dos Campos-SP, CEP: 12247-014, BrasilInstituto de Ciência e Tecnologia, Universidade Federal de São Paulo (UNIFESP) Av. Cesare M. G. Lattes, 1201, Eugênio de Mello, São José dos Campos-SP, CEP: 12247-014, BrasilWeb of Scienc
Detection of regulator genes and eQTLs in gene networks
Genetic differences between individuals associated to quantitative phenotypic
traits, including disease states, are usually found in non-coding genomic
regions. These genetic variants are often also associated to differences in
expression levels of nearby genes (they are "expression quantitative trait
loci" or eQTLs for short) and presumably play a gene regulatory role, affecting
the status of molecular networks of interacting genes, proteins and
metabolites. Computational systems biology approaches to reconstruct causal
gene networks from large-scale omics data have therefore become essential to
understand the structure of networks controlled by eQTLs together with other
regulatory genes, and to generate detailed hypotheses about the molecular
mechanisms that lead from genotype to phenotype. Here we review the main
analytical methods and softwares to identify eQTLs and their associated genes,
to reconstruct co-expression networks and modules, to reconstruct causal
Bayesian gene and module networks, and to validate predicted networks in
silico.Comment: minor revision with typos corrected; review article; 24 pages, 2
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Identifying communities by influence dynamics in social networks
Communities are not static; they evolve, split and merge, appear and
disappear, i.e. they are product of dynamical processes that govern the
evolution of the network. A good algorithm for community detection should not
only quantify the topology of the network, but incorporate the dynamical
processes that take place on the network. We present a novel algorithm for
community detection that combines network structure with processes that support
creation and/or evolution of communities. The algorithm does not embrace the
universal approach but instead tries to focus on social networks and model
dynamic social interactions that occur on those networks. It identifies
leaders, and communities that form around those leaders. It naturally supports
overlapping communities by associating each node with a membership vector that
describes node's involvement in each community. This way, in addition to
overlapping communities, we can identify nodes that are good followers to their
leader, and also nodes with no clear community involvement that serve as a
proxy between several communities and are equally as important. We run the
algorithm for several real social networks which we believe represent a good
fraction of the wide body of social networks and discuss the results including
other possible applications.Comment: 10 pages, 6 figure
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