## 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 language | English (US) |
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Title of host publication | Proceedings - 2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013 |

Pages | 74-76 |

Number of pages | 3 |

DOIs | |

State | Published - 2013 |

Externally published | Yes |

Event | 2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013 - Shanghai, China Duration: Dec 18 2013 → Dec 21 2013 |

### Other

Other | 2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013 |
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Country/Territory | China |

City | Shanghai |

Period | 12/18/13 → 12/21/13 |

## Keywords

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

## ASJC Scopus subject areas

- Biomedical Engineering