dorsal/arxiv
View SchemaSelf-learning Mutual Selection Model for Weighted Networks
| Authors | Jian-Guo Liu, Yan-Zhong Dang, Wen-Xu Wang, Zhong-Tuo Wang, Tao Zhou, Bing-Hong Wang, Qiang Guo, Zhao-Guo Xuan, Shao-Hua Jiang, Ming-Wei Zhao |
|---|---|
| Categories | |
| ArXiv ID | physics/0512270 |
| URL | https://arxiv.org/abs/physics/0512270 |
Abstract
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.
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"date_created": "2026-03-02T18:01:04.283000Z",
"date_modified": "2026-03-02T18:01:04.283000Z",
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"abstract": "In this paper, we propose a self-learning mutual selection model to\ncharacterize weighted evolving networks. By introducing the self-learning\nprobability $p$ and the general mutual selection mechanism, which is controlled\nby the parameter $m$, the model can reproduce scale-free distributions of\ndegree, weight and strength, as found in many real systems. The simulation\nresults are consistent with the theoretical predictions approximately.\nInterestingly, we obtain the nontrivial clustering coefficient $C$ and tunable\ndegree assortativity $r$, depending on the parameters $m$ and $p$. The model\ncan unify the characterization of both assortative and disassortative weighted\nnetworks. Also, we find that self-learning may contribute to the assortative\nmixing of social networks.",
"arxiv_id": "physics/0512270",
"authors": [
"Jian-Guo Liu",
"Yan-Zhong Dang",
"Wen-Xu Wang",
"Zhong-Tuo Wang",
"Tao Zhou",
"Bing-Hong Wang",
"Qiang Guo",
"Zhao-Guo Xuan",
"Shao-Hua Jiang",
"Ming-Wei Zhao"
],
"categories": [
"physics.soc-ph"
],
"title": "Self-learning Mutual Selection Model for Weighted Networks",
"url": "https://arxiv.org/abs/physics/0512270"
},
"schema_id": "dorsal/arxiv",
"source": {
"execution_id": "8e3b107a-3323-45c5-bb50-b46f57c21fd7",
"id": "arXiv Dataset IDs",
"type": "Model",
"variant": "snapshot-2026-03-01",
"version": "0.1.0"
},
"user_id": 1000002
}