GUISE: a uniform sampler for constructing frequency histogram of graphlets

Graphlet frequency distribution (GFD) has recently become popular for characterizing large networks. However, the computation of GFD for a network requires the exact count of embedded graphlets in that network, which is a computationally expensive task. As a result, it is practically infeasible to compute the GFD for even a moderately large network. In this paper, we propose Guise, which uses a Markov Chain Monte Carlo sampling method for constructing the approximate GFD of a large network. Our experiments on networks with millions of nodes show that Guise obtains the GFD with very low rate of error within few minutes, whereas the exhaustive counting-based approach takes several days.