Following the discrete algorithm presented in Ref. [28] , we first start with a random network of N nodes and m links. The average degree of the network (average number of links per node) is given by SkT~2 m N . At each time step, we select n m pairs of nodes that have at least one mutual friend. This is done by first picking n m intermediate nodes at random, before choosing two neighbours of each to become the pairs. In addition to this, we choose n r other pairs of nodes uniformly at random, with n m .n r . For every pair that is not already connected, we form a new link between them provided that both nodes have not reached the maximum number of friends L. At the end of each time step, we randomly break r existing links to simulate the friendship decay. We then calculate the clustering coefficient c of the network using the method by Schank and Wagner [29] . After the clustering coefficient stops increasing and only fluctuates about a time-independent long-run average, we say the network has converged and stop the simulation. As expected, the clustering coefficients of the converged networks are much higher than c R & SkT N for random networks with the same N and ,k..