Self-learning Mutual Selection Model for Weighted Networks
  In this paper, we propose a self-learning mutual selection model to characterize weighted evolving networks. By introducing the self-learning probability $p$ and the general mutual selection mechanism, which is controlled by the parameter $m$, the model can reproduce scale-free distributions of degree, weight and strength, as found in many real systems. The simulation results are consistent with the theoretical predictions approximately. Interestingly, we obtain the nontrivial clustering coefficient $C$ and tunable degree assortativity $r$, depending on the parameters $m$ and $p$. The model can unify the characterization of both assortative and disassortative weighted networks. Also, we find that self-learning may contribute to the assortative mixing of social networks. 
Jian-Guo Liu, Yan-Zhong Dang, Wen-Xu Wang, Zhong-Tuo Wang, Tao Zhou,   Bing-Hong Wang, Qiang Guo, Zhao-Guo Xuan, Shao-Hua Ji...