@inproceedings{5f7a9db8c94b4665bd446b31b5a1e304,
title = "Algorithmic complexity of motifs clusters superfamilies of networks",
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.",
keywords = "Algorithmic probability, Complex networks, Information content, Information theory, Kolmogorov complexity, Network motifs, Network typology",
author = "Hector Zenil and Kiani, {Narsis A.} and Jesper Tegn{\'e}r",
year = "2013",
doi = "10.1109/BIBM.2013.6732768",
language = "English (US)",
isbn = "9781479913091",
series = "Proceedings - 2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013",
pages = "74--76",
booktitle = "Proceedings - 2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013",
note = "2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013 ; Conference date: 18-12-2013 Through 21-12-2013",
}