Growing scale-free networks with tunable distributions of triad motifs

Network motifs are local structural patterns and elementary functional units of complex networks in real world, which can have significant impacts on the global behavior of these systems.Manymodels are able to reproduce complexnetworksmimicking a series of global features of real systems, however the local features such asmotifs in real networks have not beenwell represented.Wepropose amodel to grow scale-free networkswith tunablemotif distributions through a combined operation of preferential attachment and triad motif seeding steps. Numerical experiments show that the constructed networks have adjustable distributions of the local triad motifs, meanwhile preserving the global features of powerlaw distributions of node degree, short average path lengths of nodes, and highly clustered structures. © 2015 Elsevier B.V. All rights reserved.