An efficient and scalable algorithmic method for generating large: scale random graphs
HiPC 2016
Pages: 32
Published: Nov 13, 2016
Abstract
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 Erdős-Renyi (BTER) models have been devised to capture those properties. However, the generative algorithms for these models are...
Paper Details
Title
An efficient and scalable algorithmic method for generating large: scale random graphs
Published Date
Nov 13, 2016
Pages
32
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