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中文摘要：
在Barrat，Barthélemy和Vespignani（BBV）加权无标度网络模型的基础上，提出了一种可大范围调节聚类系数的加权无标度网络模型——广义BBV模型（GBBV模型）．理论分析和仿真实验表明，GBBV模型保留了BBV模型的许多特征，节点度、节点权重和边权值等都服从幂律分布。但是，GBBV模型克服了BBV模型只能小范围调节聚类系数的缺陷，从而可以用于具有大聚类系数网络的建模．
英文摘要：
Based on the weighted scale-free network model proposed by Barrat, Barthélemy and Vespignani （BBV）, we propose a generalized BBV （GBBV） model with large-scale tunable clustering coefficient. Theoretical analysis and numerical simulations show that the GBBV model retains many properties of the BBV model, such as power-law distributions of node degree, node strength and edge weight. However, the GBBV model overcomes the drawback of the BBV model that the clustering coefficient can only be tuned in a small interval, and therefore the GBBV model can be used for modeling networks with large clustering coefficients.
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