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- W2765310599 abstract "One of the most popular methods of estimating the complexity of networks is to measure the entropy of network invariants, such as adjacency matrices or degree sequences. Unfortunately, entropy and all entropy-based information-theoretic measures have several vulnerabilities. These measures neither are independent of a particular representation of the network nor can capture the properties of the generative process, which produces the network. Instead, we advocate the use of the algorithmic entropy as the basis for complexity definition for networks. Algorithmic entropy (also known as Kolmogorov complexity or <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML id=M1><mml:mrow><mml:mi>K</mml:mi></mml:mrow></mml:math>-complexity for short) evaluates the complexity of the description required for a lossless recreation of the network. This measure is not affected by a particular choice of network features and it does not depend on the method of network representation. We perform experiments on Shannon entropy and <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML id=M2><mml:mrow><mml:mi>K</mml:mi></mml:mrow></mml:math>-complexity for gradually evolving networks. The results of these experiments point to <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML id=M3><mml:mrow><mml:mi>K</mml:mi></mml:mrow></mml:math>-complexity as the more robust and reliable measure of network complexity. The original contribution of the paper includes the introduction of several new entropy-deceiving networks and the empirical comparison of entropy and <mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML id=M4><mml:mrow><mml:mi>K</mml:mi></mml:mrow></mml:math>-complexity as fundamental quantities for constructing complexity measures for networks." @default.
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- W2765310599 date "2017-01-01" @default.
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- W2765310599 title "On Measuring the Complexity of Networks: Kolmogorov Complexity versus Entropy" @default.
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- W2765310599 doi "https://doi.org/10.1155/2017/3250301" @default.
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