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Descripción
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| In this paper we present a study of the influence of heterogeneity on the clustering of preferential attachment networks. The study is performed by the numerical analysis of the threshold preferential attachment model, a generalization of the Barab¿asi¿Albert model to heterogeneous complex networks. Heterogeneous networks are characterized by the existence of intrinsic properties of the nodes which induce specific affinities in their interactions. We analyze the influence of the affinity parameters on the distribution of degree-averaged clustering coefficients of the threshold model. We show that the introduction of heterogeneity increases the inverse correlation between clustering and connectivity of the nodes, inducing a power-law scaling in the clustering distribution. We also show that a higher level of heterogeneity increases the overall clustering coefficients irrespective of the node degrees. These results exhibit a better agreement of the extended model with the empirical observations of clustering in real networks. | |
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Internacional
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Si |
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JCR del ISI
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Si |
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Título de la revista
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INTERNATIONAL JOURNAL OF BIFURCATION AND CHAOS |
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ISSN
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0218-1274 |
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Factor de impacto JCR
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0,87 |
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Información de impacto
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Volumen
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19 |
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DOI
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10.1142/S0218127409023445 |
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Número de revista
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3 |
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Desde la página
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1029 |
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1036 |
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