Mutual attraction model for both assortative and disassortative weighted networks.
Mutual attraction model for both assortative and disassortative weighted networks.
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DOI：
10.1103/physreve.73.016133
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发表时间：
2005-05
期刊：
影响因子：
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通讯作者：
Wenxu Wang;Bo Hu;B. Wang;G. Yan
中科院分区：
文献类型：
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作者：
Wenxu Wang;Bo Hu;B. Wang;G. Yan
对于大多数复杂网络，一对节点之间的连接是它们相互亲和和依附的结果。在这封信中，我们将提出一个相互吸引模型来描述加权进化网络。通过引入初始吸引力A和相互吸引的一般机制（由参数m控制），我们的模型可以自然地再现度、重量和强度的无标度分布，就像在许多实际系统中发现的那样。同时，仿真结果与理论预测相吻合。有趣的是，根据m和a的值，我们获得了非平凡的聚类系数C和可调的分类度r。我们的模型似乎是一个更一般的模型，统一了分类和非分类加权网络的特征。
For most complex networks, the connection between a pair of nodes is the result of their mutual affinity and attachment. In this letter, we will propose a mutual attraction model to characterize weighted evolving networks. By introducing the initial attractiveness A and the general mechanism of mutual attraction (controlled by parameter m), our model can naturally reproduce scale-free distributions of degree, weight, and strength, as found in many real systems. Also, simulation results are consistent with theoretical predictions. Interestingly, we obtain nontrivial clustering coefficient C and tunable degree assortativity r, depending on the values of m and A. Our model appears as a more general one that unifies the characterization of both assortative and disassortative weighted networks.