  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.

Physics and Society