In this paper, we develop a general analytical method to compute clustering coefficients of growing networks. This method can be applied to any network as long as we can construct and solve the dynamic equation for the degree of any node. We also verify the accuracy of the method through simulation.

Preferential attachment is widely recognised as the principal driving force behind the evolution of many growing networks, and measuring the extent to which it occurs during the growth of a network is important for explaining its overall structure. Conventional methods require that the timeline...

A new model of the clustering-driven growing network is presented and studied. Numerical and analytical results show that the pure clustering-driven addition of the certain pattern is not responsible for the resulting network hierarchy and scale-free structure. Introduction of local rules leads...