Research on community structure detection in complex networks has attracted a great deal of attention in recent *** this paper we propose a random iterative algorithm to uncover meaningful *** algorithm starts with in...
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Research on community structure detection in complex networks has attracted a great deal of attention in recent *** this paper we propose a random iterative algorithm to uncover meaningful *** algorithm starts with initial population *** individual of the population is encoded with the community identifiers of the nodes in the network,so it is a potential solution of the community structure of the network *** are randomly assigned into communities at the beginning of the *** each iteration some nodes are randomly selected,their community identifiers are reassigned according to the modularity function and the measure of information discrepancy based on the shortest path profiles of nodes in the *** the end,a proper community structure can be detected by the identifiers encoded in the individual with the largest *** algorithm does not need any prior knowledge about the number of communities and can give an appropriate number by maximizing the modularity *** computational results of the method on real-world networks confirm its capability.
Hypertension,among diabetes,obesity and others,is one of the common human diseases that is genetically expressed as complex traits to which genetic,environmental,and demographic factors contribute *** the underlying g...
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Hypertension,among diabetes,obesity and others,is one of the common human diseases that is genetically expressed as complex traits to which genetic,environmental,and demographic factors contribute *** the underlying genes and examining their interactions, a crucial step in understanding the molecular pathogenesis of complex diseases,is both a statistical and a computational challenge,stressing the need for novel strategies to move this process *** this paper we propose a new method to study the association of multiple gene interactions for complex *** method is carried out by two ***,we sequentially select additionally associated SNP loci combinations by minimizing the p-value of a test based on an information measure,measure of information ***,this approach is called MID ***,the significance of the selected associated loci combinations is assessed by an x *** MID method is model-free and nonparametric,it is easy to compute and *** capability of the MID method is confirmed by applying it to investigate the multiple gene interactions on risk of hypertension in northern Han Chinese,where thirty-three SNP loci with three-genotype in eleven candidate genes are *** results are consistent with those of Gu et al（2006）.Additionally,we get some other new *** indicates that our idea is indeed feasible and useful in practice.
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