Self-learning Mutual Selection Model for Weighted Networks

Liu, Jian-Guo; Dang, Yan-Zhong; Wang, Wen-Xu; Wang, Zhong-Tuo; Zhou, Tao; Wang, Bing-Hong; Guo, Qiang; Xuan, Zhao-Guo; Jiang, Shao-Hua; Zhao, Ming-Wei

Self-learning Mutual Selection Model for Weighted Networks

Authors

Description

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.
5 pages, 5 figures