Growing Scale-free Small-world Networks with Tunable Assortative   Coefficient
  In this paper, we propose a simple rule that generates scale-free small-world networks with tunable assortative coefficient. These networks are constructed by two-stage adding process for each new node. The model can reproduce scale-free degree distributions and small-world effect. The simulation results are consistent with the theoretical predictions approximately. Interestingly, we obtain the nontrivial clustering coefficient $C$ and tunable degree assortativity $r$ by adjusting the parameter: the preferential exponent $\beta$. The model can unify the characterization of both assortative and disassortative networks. 
Qiang Guo, Tao Zhou, Jian-Guo Liu, Wen-Jie Bai, Bing-Hong Wang, and   Ming Zhao