Direct Generation of Random Graphs Exactly Realising a Prescribed Degree Sequence

Abstract : This paper intends to extend the possibilities available to researchers for the evaluation of directed networks with the use of randomly generated graphs. The direct generation of a simple network with a prescribed degree sequence still seems to be an open issue, since the prominent configuration model usually does not realise the degree distribution exactly. We propose such an algorithm using a heuristic for node prioritisation. We demonstrate that the algorithm samples approximately uniformly. In comparison to the switching Markov Chain Monte Carlo algorithms, the direct generation of edges allows an easy modification of the linking behaviour in the random graph, introducing for example degree correlations, mixing patterns or community structure. That way, more specific random graphs can be generated (non-uniformly) in order to test hypotheses on the question, whether specific network features are due to a specific linking behaviour only. Or it can be used to generate series of synthetic benchmark networks with a specific community structure, including hierarchies and overlaps.
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Darko Obradovic, Maximilien Danisch. Direct Generation of Random Graphs Exactly Realising a Prescribed Degree Sequence. The 6th International Conference on Computational Aspects of Social Networks, Jul 2014, Porto, Portugal. ⟨hal-01208580⟩

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