Growing scale-free networks with small-world behavior

In the context of growing networks, we introduce a simple dynamical model that unifies the generic features of real networks: scale-free distribution of degree and the small-world effect. While the average shortest path length increases logarithmically as in random networks, the clustering coefficient assumes a large value independent of system size. We derive analytical expressions for the clustering coefficient in two limiting cases: random $\left[C\sim \right(\ln {N)}^2/N]$ and highly clustered $\mathrm{}(C=5/6\mathrm{})\mathrm{}$ scale-free networks.