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Analytical Formulation of the Block-Constrained Configuration Model
[article]
2018
arXiv
pre-print
We provide a novel family of generative block-models for random graphs that naturally incorporates degree distributions: the block-constrained configuration model. Block-constrained configuration models build on the generalised hypergeometric ensemble of random graphs and extend the well-known configuration model by enforcing block-constraints on the edge generation process. The resulting models are analytically tractable and practical to fit even to large networks. These models provide a new,
arXiv:1811.05337v1
fatcat:h2ctanpzevcvjiczug6zp6gi4i