Algorithmic complexity of motifs clusters superfamilies of networks

Hector Zenil, Narsis A. Kiani, Jesper Tegnér

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

Abstract

Representing biological systems as networks has proved to be very powerful. For example, local graph analysis of substructures such as subgraph overrepresentation (or motifs) has elucidated different sub-types of networks. Here we report that using numerical approximations of Kolmogorov complexity, by means of algorithmic probability, clusters different classes of networks. For this, we numerically estimate the algorithmic probability of the sub-matrices from the adjacency matrix of the original network (hence including motifs). We conclude that algorithmic information theory is a powerful tool supplementing other network analysis techniques.

Original languageEnglish (US)
Title of host publicationProceedings - 2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013
Pages74-76
Number of pages3
DOIs
StatePublished - 2013
Externally publishedYes
Event2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013 - Shanghai, China
Duration: Dec 18 2013Dec 21 2013

Other

Other2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013
CountryChina
CityShanghai
Period12/18/1312/21/13

Keywords

  • Algorithmic probability
  • Complex networks
  • Information content
  • Information theory
  • Kolmogorov complexity
  • Network motifs
  • Network typology

ASJC Scopus subject areas

  • Biomedical Engineering

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