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Random networks are widely used to model complex networks and research theirproperties. In order to get a good approximation of complex networksencountered in various disciplines of science, the ability to tune variousstatistical properties of random networks is very important. In this manuscriptwe present an algorithm which is able to construct arbitrarily degree-degreecorrelated networks with adjustable degree-dependent clustering. We verify thealgorithm by using empirical networks as input and describe additionally asimple way to fix a degree-dependent clustering function if degree-degreecorrelations are given.

Generating random networks with given degree-degree correlations and degree-dependent clustering