Many real-world systems and networks are modeled and analyzed using various random graph models. These models must incorporate relevant properties such as degree distribution and clustering coefficient. Many models, such as the Chung-Lu (CL), stochastic Kronecker, stochastic block model (SBM), and block two–level Erdos-Renyi (BTER) models have been devised to capture those properties. Though real-world networks are sparse, generating large instances from these models, with millions of nodes, is computationally expensive and infeasible with sequential algorithms. This motivates parallel algorithms for generating instances from different random graph models. We present a novel time and space efficient algorithmic method to generate random graphs for the CL, BTER, and SBM models, which are near optimal, in terms of time and space. We compare our approach with other state-of-the-art methods and show significant improvement in performance.

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