Explaining the emergence of complex networks through log-normal fitness in a Euclidean node similarity space.
Explaining the emergence of complex networks through log-normal fitness in a Euclidean node similarity space.
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DOI：
10.1038/s41598-021-81547-3
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发表时间：
2021-01-21
影响因子：
4.6
通讯作者：
Smith KM
中科院分区：
文献类型：
--
作者：
Smith KM
不同现象的网络--无论是全球生态、人类社会机构、人脑内，还是在微尺度的蛋白质相互作用中--呈现出大致一致的结构特征。为了解释这一点，我们提出了一种新的理论，其中链路概率由对数正态节点适应度(表面)因子和潜在欧氏空间嵌入的节点相似性(深度)因子来建模。根据文献中反复出现的趋势，该理论断言，联系的产生是由于个人主义信息和二元信息，而构成所谓深度因素的重要二元信息被组成表面因素的本质上的非二元信息所掩盖。基于这一理论的模型在110个网络上的表现远远超过了流行的幂定律适应度和双曲几何解释。重要的是，该模型的度分布在小密度下类似于幂定律，在较大密度下类似于对数正态分布，为长期存在的关于无标度网络的性质和存在的争论提供了一个调和的解决方案。为了验证这一理论，在经济世界城市网络和功能磁共振连接体上的表面因子反演方法导致了几何排列更接近的最近邻网络，正如深度因子假设的情况一样。这为理解、分析、解构和解释网络现象奠定了新的基础。
Networks of disparate phenomena—be it the global ecology, human social institutions, within the human brain, or in micro-scale protein interactions—exhibit broadly consistent architectural features. To explain this, we propose a new theory where link probability is modelled by a log-normal node fitness (surface) factor and a latent Euclidean space-embedded node similarity (depth) factor. Building on recurring trends in the literature, the theory asserts that links arise due to individualistic as well as dyadic information and that important dyadic information making up the so-called depth factor is obscured by this essentially non-dyadic information making up the surface factor. Modelling based on this theory considerably outperforms popular power-law fitness and hyperbolic geometry explanations across 110 networks. Importantly, the degree distributions of the model resemble power-laws at small densities and log-normal distributions at larger densities, posing a reconciliatory solution to the long-standing debate on the nature and existence of scale-free networks. Validating this theory, a surface factor inversion approach on an economic world city network and an fMRI connectome results in considerably more geometrically aligned nearest neighbour networks, as is hypothesised to be the case for the depth factor. This establishes new foundations from which to understand, analyse, deconstruct and interpret network phenomena.
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DOI：
10.1073/pnas.1220826110
发表时间：
2013-07-09
影响因子：
11.1
作者：
Crossley, Nicolas A.;Mechelli, Andrea;Bullmore, Edward T.
通讯作者：
Bullmore, Edward T.
影响因子：
8.6
作者：
Del Genio, Charo I.;Gross, Thilo;Bassler, Kevin E.
通讯作者：
Bassler, Kevin E.
影响因子：
56.9
作者：
Barabási, AL;Albert, R
通讯作者：
Albert, R
影响因子：
0.8
作者：
Eppstein, D;Paterson, MS;Yao, FF
通讯作者：
Yao, FF
DOI：
10.1088/1742-5468/2008/10/p10008
发表时间：
2008-10-01
影响因子：
2.4
作者：
Blondel, Vincent D.;Guillaume, Jean-Loup;Lefebvre, Etienne
通讯作者：
Lefebvre, Etienne