Correlation of automorphism group size and topological properties with program-size complexity evaluations of graphs and complex networks

Hector Zenil, Fernando Soler-Toscano, Kamaludin Dingle and Ard A. Louis

Physica A: Statistical Mechanics and its Applications, 2014, vol. 404, issue C, 341-358

Abstract: We show that numerical approximations of Kolmogorov complexity (K) of graphs and networks capture some group-theoretic and topological properties of empirical networks, ranging from metabolic to social networks, and of small synthetic networks that we have produced. That K and the size of the group of automorphisms of a graph are correlated opens up interesting connections to problems in computational geometry, and thus connects several measures and concepts from complexity science. We derive these results via two different Kolmogorov complexity approximation methods applied to the adjacency matrices of the graphs and networks. The methods used are the traditional lossless compression approach to Kolmogorov complexity, and a normalised version of a Block Decomposition Method (BDM) based on algorithmic probability theory.

Keywords: Kolmogorov complexity; Complex networks; Graph automorphisms; Algorithmic probability; Compressibility; Network biology (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:404:y:2014:i:c:p:341-358

DOI: 10.1016/j.physa.2014.02.060

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