Deciphering the generating rules and functionalities of complex networks.

Deciphering the generating rules and functionalities of complex networks.
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DOI:
10.1038/s41598-021-02203-4
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发表时间:
2021-11-25
期刊:
影响因子:
4.6
通讯作者:
Bogdan P
Bogdan P
中科院分区:
综合性期刊3区
文献类型:
--
作者:
Xiao X;Chen H;Bogdan P

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网络理论可以帮助我们理解,分析,建模和设计各种复杂系统。复杂的网络编码自然界各种系统的复杂拓扑和结构相互作用。为了挖掘自然和技术系统的多尺度耦合,异质性和复杂性,我们需要表达和严格的数学工具,这些工具可以帮助我们了解复杂网络及其相互关系的增长,拓扑,动态,多尺度结构以及功能。为此,我们构建了基于节点的分形维(NFD)和基于节点的多重分析分析(NMFA)框架,以揭示生成规则并量化动态复杂网络的规模依赖性拓扑和多重型特征。我们提出了用于测量网络结构的复杂性,异质性和不对称程度的新指标,以及网络之间的结构距离。这种形式主义为学习网络系统中的能量和相变提供了新的见解,并可以帮助我们了解管理网络发展的多个生成机制。
Network theory helps us understand, analyze, model, and design various complex systems. Complex networks encode the complex topology and structural interactions of various systems in nature. To mine the multiscale coupling, heterogeneity, and complexity of natural and technological systems, we need expressive and rigorous mathematical tools that can help us understand the growth, topology, dynamics, multiscale structures, and functionalities of complex networks and their interrelationships. Towards this end, we construct the node-based fractal dimension (NFD) and the node-based multifractal analysis (NMFA) framework to reveal the generating rules and quantify the scale-dependent topology and multifractal features of a dynamic complex network. We propose novel indicators for measuring the degree of complexity, heterogeneity, and asymmetry of network structures, as well as the structure distance between networks. This formalism provides new insights on learning the energy and phase transitions in the networked systems and can help us understand the multiple generating mechanisms governing the network evolution.
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