Scale-free networks are characterized by a degree distribution with power-lawbehavior and have been shown to arise in many areas, ranging from the WorldWide Web to transportation or social networks. Degree distributions of observednetworks, however, often differ from the power-law type and data basedinvestigations require modifications of the typical scale-free network. We present an algorithm that generates networks in which the skewness of thedegree distribution is tuneable by modifying the preferential attachment stepof the Barabasi-Albert construction algorithm. Skewness is linearly correlatedwith the maximal degree of the network and, therefore, adequately representsthe influence of superspreaders or hubs. By combining our algorithm with workof Holme and Kim, we show how to generate networks with skewness gamma andclustering coefficient kappa, over a wide range of values.
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