A weighted scale-free network model with large-scale tunable clustering

Based on the weighted scale-free network model proposed by Barrat, Barthelemy 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.