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Transcription networks, and other directed networks can be characterized bysome topological observables such as for example subgraph occurrence (networkmotifs). In order to perform such kind of analysis, it is necessary to be ableto generate suitable randomized network ensembles. Typically, one considersnull networks with the same degree sequences of the original ones. The commonlyused algorithms sometimes have long convergence times, and sampling problems.We present here an alternative, based on a variant of the importance samplingMontecarlo developed by Chen et al. [1].

DIA-MCIS. An Importance Sampling Network Randomizer for Network Motif Discovery and Other Topological Observables in Transcription Networks