A Faster Algorithm for Detecting Network Motifs
Motifs in a network are small connected subnetworks that occur in significantly higher frequencies than in random networks. They have recently gathered much attention as a useful concept to uncover structural design principles of complex networks. Kashtan et al. [Bioinformatics, 2004] proposed a sampling algorithm for efficiently performing the computationally challenging task of detecting network motifs. However, among other drawbacks, this algorithm suffers from sampling bias and is only efficient when the motifs are small (3 or 4 nodes). Based on a detailed analysis of the previous algorithm, we present a new algorithm for network motif detection which overcomes these drawbacks. Experiments on a testbed of biological networks show our algorithm to be orders of magnitude faster than previous approaches. This allows for the detection of larger motifs in bigger networks than was previously possible, facilitating deeper insight into the field.
KeywordsRandom Graph Random Network Input Graph Network Motif Degree Sequence
Unable to display preview. Download preview PDF.
- 2.Artzy-Randrup, Y., Fleishman, S.J., Ben-Tal, N., Stone, L.: Comment on network motifs: Simple building blocks of complex networks and superfamilies of designed and evolved networks. Science 305, 1007c (2004)Google Scholar
- 7.Itzkovitz, S., Levitt, R., Kashtan, N., et al.: Coarse-graining and self-dissimilarity of complex networks. Phys. Rev. E 71(016127) (2005)Google Scholar
- 8.Itzkovitz, S., Milo, R., Kashtan, N., et al.: Subgraphs in random networks. Phys. Rev. E 68(26127) (2003)Google Scholar
- 10.Knuth, D.E.: Estimating the efficiency of backtrack programs. In: Selected papers on Analysis of Algorithms. Stanford Junior University, Palo Alto (2000)Google Scholar
- 13.Milo, R., Itzkovitz, S., Kashtan, N., et al.: Response to comment on network motifs: Simple building blocks of complex networks and superfamilies of designed and evolved networks. Science 305, 1007d (2004)Google Scholar
- 16.Newman, M.E.J., Strogatz, S.H., Watts, D.J.: Random graphs with arbitrary degree distributions and their applications. Phys. Rev. E 64(026118) (2001)Google Scholar