Self-learning Mutual Selection Model for Weighted Networks

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dc.creatorLiu, Jian-Guo
dc.creatorDang, Yan-Zhong
dc.creatorWang, Wen-Xu
dc.creatorWang, Zhong-Tuo
dc.creatorZhou, Tao
dc.creatorWang, Bing-Hong
dc.creatorGuo, Qiang
dc.creatorXuan, Zhao-Guo
dc.creatorJiang, Shao-Hua
dc.creatorZhao, Ming-Wei
dc.date2005-12-30
dc.date.accessioned2026-07-07T06:56:23Z
dc.date.available2026-07-07T06:56:23Z
dc.descriptionIn 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.
dc.description5 pages, 5 figures
dc.identifierhttps://arxiv.org/abs/physics/0512270
dc.identifierhttp://arxiv.org/abs/physics/0512270
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/106620
dc.subjectPhysics and Society
dc.titleSelf-learning Mutual Selection Model for Weighted Networks
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