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- W1944295417 abstract "We show that numerical approximations of Kolmogorov complexity (K) applied to graph adjacency matrices capture some group-theoretic and topological properties of graphs and empirical networks ranging from metabolic to social networks. 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 show that approximations of K characterise synthetic and natural networks by their generating mechanisms, assigning lower algorithmic randomness to complex network models (Watts-Strogatz and Barabasi-Albert networks) and high Kolmogorov complexity to (random) Erdos-Renyi graphs. 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) measure, based on algorithmic probability theory." @default.
- W1944295417 created "2016-06-24" @default.
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- W1944295417 date "2013-06-03" @default.
- W1944295417 modified "2023-09-27" @default.
- W1944295417 title "Graph Automorphism and Topological Characterization of Synthetic and Natural Complex Networks by Information Content." @default.
- W1944295417 hasPublicationYear "2013" @default.
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