DIA-MCIS: An importance sampling network randomizer for network motif discovery and other topological observables in transcription networks

Motivation: Transcription networks, and other directed networks can be characterized by some topological observables (e.g. network motifs), that require a suitable randomized network ensemble, typically with the same degree sequences of the original ones. The commonly used algorithms sometimes have long convergence times, and sampling problems. We present here an alternative, based on a variant of the importance sampling Monte Carlo developed by (Chen et al.).