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Several data management applications call for the generation of random graphs. For example, such generation is needed for the synthesis of data sets aiming to evaluate efficiency and effectiveness of algorithms, for simulating processes, and at the heart of randomized algorithms. Furthermore, a random-graph generation process can be leveraged for sampling. Thanks to the versatility of graphs as data representation model, random graph generation processes are also relevant in applications ranging from physics and biology to sociology.
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