Multistage Random Growing Small-World Networks with Power-law degree   Distribution
  In this paper, a simply rule that generates scale-free networks with very large clustering coefficient and very small average distance is presented. These networks are called {\bf Multistage Random Growing Networks}(MRGN) as the adding process of a new node to the network is composed of two stages. The analytic results of power-law exponent $\gamma=3$ and clustering coefficient $C=0.81$ are obtained, which agree with the simulation results approximately. In addition, the average distance of the networks increases logarithmical with the number of the network vertices is proved analytically. Since many real-life networks are both scale-free and small-world networks, MRGN may perform well in mimicking reality. 
Jian-Guo Liu, Yan-Zhong Dang, and Zhong-tuo Wang