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