1. A computer-implemented method comprising: receiving, with one or more processors, social graph data associated with a social network, the social graph data including nodes, edges that connect the nodes and weights associated with the edges in a social graph; determining, with the one or more processors, a first probability of existence of an edge in the social graph based on the weights; identifying, with the one or more processors, a number of sets of neighbors for a first node; determining, with the one or more processors, a second probability that the first node forms a triangle with a pair of neighbor nodes by computing a third probability of selecting the pair of neighbor nodes from the number of sets of neighbors based on the pair of neighbor nodes being the first two nodes in a total number of random permutations of the sets of neighbors; and computing, with the one or more processors, a weighted clustering coefficient for the first node based on the first and second probabilities.