Many networks are proved to have community structure. Dense communities have been intensively investigated in recent years, oppositely seldom attention has been paid to sparse ones, which refer to those communities in...
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Many networks are proved to have community structure. Dense communities have been intensively investigated in recent years, oppositely seldom attention has been paid to sparse ones, which refer to those communities in...
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ISBN:
(纸本)9781457716614
Many networks are proved to have community structure. Dense communities have been intensively investigated in recent years, oppositely seldom attention has been paid to sparse ones, which refer to those communities induced by low or middle-degree nodes rather than high-degree components. Recently, it has gradually been recognized that sparse community is also an important structure in biological networks because most disease genes and drug targets are within it. In this paper, we propose a dynamical method to extract sparse communities in complex networks by constructing local synchronization properties of phase oscillators. Compared to dense communities, sparse ones provide more general building and functional blocks in the networks without emphasis on the dominance of internal degrees over outside ones as well as the constraints of high degree connectors.
Community detection has attracted a great deal of attention in recent years. A parsimony criterion for detecting this structure means that as minimal as possible number of inserted and deleted edges is needed when we ...
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Community detection has attracted a great deal of attention in recent years. A parsimony criterion for detecting this structure means that as minimal as possible number of inserted and deleted edges is needed when we make the network considered become a disjoint union of cliques. However, many small groups of nodes are obtained by directly using this criterion to some networks especially for sparse ones. In this paper we propose a weighted parsimony model in which a weight coefficient is introduced to balance the inserted and deleted edges to ensure the obtained subgraphs to be reasonable communities. Some benchmark testing examples are used to validate the effectiveness of the proposed method. It is interesting that the weight here can be determined only by the topological features of the network. Meanwhile we make some comparison of our model with maximizing modularity Q and modularity density D on some of the benchmark networks, although sometimes too many or a little less numbers of communities are obtained with Q or D, a proper number of communities are detected with the weighted model. All the computational results confirm its capability for community detection for the small or middle size networks.
Modularity Q has been broadly used as a valid measure for community detection in complex networks. Fortunato and Barthe ′lemy later proposed that modularity contains an intrinsic scale that depends on the total numbe...
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Modularity Q has been broadly used as a valid measure for community detection in complex networks. Fortunato and Barthe ′lemy later proposed that modularity contains an intrinsic scale that depends on the total number of links in the network. But some extra restrictions on some parameters are needed for their analysis. In this paper we further study this problem. Here we give general analysis and more details to show that the resolution limit of Q depends not only on the total links but also on the degree of interconnectedness between pairs of communities. Without any constraint imposed on the parameters, there exists a proper area of the validity (or invalidity) of Q, which is deduced by the definitions of community structure and modularity Q, all these make the analysis here more reasonable.
Many real-world networks have a common feature of organization,i.e.,community *** this structure is fundamental for uncovering the links between the structure and the function in complex networks and for practical app...
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Many real-world networks have a common feature of organization,i.e.,community *** this structure is fundamental for uncovering the links between the structure and the function in complex networks and for practical applications in many disciplines such as biology and *** this paper we propose a weighted parsimony criterion for community detection in complex *** criterion relates communities with cliques(or complete subgraphs).Parsimony here means that as minimal as possible number of inserted and deleted edges is needed when we make the network considered become a disjoint union of cliques.A weight based on the topological features of the network is introduced to ensure the obtained subgraphs to be communities by balancing the inserted and deleted *** on real networks give excellent results.
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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