A highly clustered scale-free network evolved by random walking

In present paper, we propose a highly clustered weighted network model that incorporates the addition of a new node with some links, new links between existing nodes and the edge's weight dynamical evolution based on weight-dependent walks at each time step. The analytical approach and numerical simulation show that the system grows into a weighted network with the power-law distributions of strength, weight and degree. The weight-dependent walk length l will not influence the strength distribution, but the clustering coefficient of the network is sensitive to l. Particularly, the clustering coefficient is especially high and almost independent of the network size when l=2.