Evolving scale-free network model with tunable clustering

The Barabasi-Albert (BA) model is extended to include the concept of local world and the microscopic event of adding edges. With probability p, we add a new node with m edges which preferentially link to the nodes presented in the network; with probability 1 - p, we add m edges among the present nodes. A node is preferentially selected by its degree to add an edge randomly among its neighbors. Using the continuum theory and the rate equation method we get the analytical expressions of the power-law degree distribution with exponent gamma = 3 and the clustering coefficient c(k) similar to k(-1) + c. The analytical expressions are in good agreement with the numerical calculations.