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The Central Limit Theorem is useful when analyzing large data sets because it assumes that the sampling distribution of the mean will be normally distributed and typically form a bell curve.

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and the performance of these algorithms has been quantified through metrics based on graph structural properties preserved by the sampling: degree distribution, clustering coefficient, and others.