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

TL;DR

This paper introduces DIA-MCIS, an importance sampling network randomizer that efficiently generates null models for directed networks, aiding in the analysis of network motifs and topological features.

Contribution

It proposes a novel importance sampling Monte Carlo method tailored for directed networks, improving sampling efficiency over traditional algorithms.

Findings

Faster convergence in generating randomized networks.

More accurate estimation of motif frequencies.

Applicable to transcription and other directed networks.

Abstract

Transcription networks, and other directed networks can be characterized by some topological observables such as for example subgraph occurrence (network motifs). In order to perform such kind of analysis, it is necessary to be able to generate suitable randomized network ensembles. Typically, one considers null networks 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 Montecarlo developed by Chen et al. [1].