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Over the last decade a lot of common properties were found in complex networks in several fields such as sociology, biology and computer engineering. Recently, the multifractal network generator method has been developed, and it seems to be a promising way to generate networks with prescribed statistical properties. For educational purposes, however, it would be adequate to create an easy-to-use redevelopment framework. Therefore, a software package had been developed in Python language that can generate a network with a given degree distribution or a given average degree using the multifractal generator method. This package is a part of the cxnet framework, which itself is suitable for educational applications. The present paper discusses the reasons why this framework was developed in Python. Those parts of the program that need longer running times were identified and rewritten in C++. Running times of the generations were measured, changing several parameters, and the new version turned out to be an order of magnitude faster.

Running Time Comparison of Two Realizations of the Multifractal Network Generator Method