Finding Network Motifs Using MCMC Sampling
Scientists have shown that network motifs are key building block of various biological networks. Most of the existing exact methods for finding network motifs are inefficient simply due to the inherent complexity of this task. In recent years, researchers are considering approximate methods that save computation by sacrificing exact counting of the frequency of potential motifs. However, these methods are also slow when one considers the motifs of larger size. In this work, we propose two methods for approximate motif finding, namely SRW-rw, and MHRW based on Markov Chain Monte Carlo (MCMC) sampling. Both the methods are significantly faster than the best of the existing methods, with comparable or better accuracy. Further, as the motif size grows the complexity of the proposed methods grows linearly.
KeywordsRandom Walk Markov Chain Monte Carlo Input Graph Network Motif Proposal Distribution
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