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两条普遍的物理原理塑造了现实世界网络的幂律统计特性。

Two universal physical principles shape the power-law statistics of real-world networks.

作者信息

Lorimer Tom, Gomez Florian, Stoop Ruedi

机构信息

Institute of Neuroinformatics and Institute of Computational Science, University of Zurich and ETH Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland.

出版信息

Sci Rep. 2015 Jul 23;5:12353. doi: 10.1038/srep12353.

Abstract

The study of complex networks has pursued an understanding of macroscopic behaviour by focusing on power-laws in microscopic observables. Here, we uncover two universal fundamental physical principles that are at the basis of complex network generation. These principles together predict the generic emergence of deviations from ideal power laws, which were previously discussed away by reference to the thermodynamic limit. Our approach proposes a paradigm shift in the physics of complex networks, toward the use of power-law deviations to infer meso-scale structure from macroscopic observations.

摘要

复杂网络的研究一直致力于通过关注微观可观测量中的幂律来理解宏观行为。在此,我们揭示了复杂网络生成所基于的两个普遍的基本物理原理。这些原理共同预测了与理想幂律的偏差的一般出现,而这些偏差此前通过参考热力学极限被忽略了。我们的方法在复杂网络物理学中提出了一种范式转变,即利用幂律偏差从宏观观测中推断中尺度结构。

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