Hidden network generating rules from partially observed complex networks
Hidden network generating rules from partially observed complex networks
复制标题
隐藏网络从部分观察的复杂网络生成规则
DOI：
10.1038/s42005-021-00701-5
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发表时间：
2021
影响因子：
5.5
通讯作者：
Bogdan, Paul
中科院分区：
文献类型：
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作者：
Yang, Ruochen;Sala, Frederic;Bogdan, Paul
复杂的生物、神经科学、地球科学和社交网络都表现出异质的自相似高阶拓扑结构，这些拓扑结构通常具有多重分形的性质。然而，通过一个紧凑的数学描述来描述它们的拓扑复杂性，并破译它们的拓扑控制规则仍然是难以捉摸的，并阻止了对网络的全面理解。为了克服这一挑战，我们提出了一个加权多重分形图模型，能够捕捉复杂系统的基本生成规则，并表征其节点的异质性和成对的相互作用。为了推导含有隐藏信息的生成测度，我们引入了一个变分期望最大化框架。我们证明了网络生成器重建的鲁棒性作为模型属性的函数，特别是在嘈杂和部分观察的情况下。所提出的网络生成器推理框架能够再现网络特性，区分大脑网络和染色体相互作用中的不同结构，并检测人类基因组构象图中的拓扑关联域区域。
Complex biological, neuroscience, geoscience and social networks exhibit heterogeneous self-similar higher order topological structures that are usually characterized as being multifractal in nature. However, describing their topological complexity through a compact mathematical description and deciphering their topological governing rules has remained elusive and prevented a comprehensive understanding of networks. To overcome this challenge, we propose a weighted multifractal graph model capable of capturing the underlying generating rules of complex systems and characterizing their node heterogeneity and pairwise interactions. To infer the generating measure with hidden information, we introduce a variational expectation maximization framework. We demonstrate the robustness of the network generator reconstruction as a function of model properties, especially in noisy and partially observed scenarios. The proposed network generator inference framework is able to reproduce network properties, differentiate varying structures in brain networks and chromosomal interactions, and detect topologically associating domain regions in conformation maps of the human genome.
影响因子：
4.6
作者：
Chenzhong Yin;Xiongye Xiao;Valeriu Balaban;M. Kandel;Y. J. Lee;G. Popescu;P. Bogdan
通讯作者：
Chenzhong Yin;Xiongye Xiao;Valeriu Balaban;M. Kandel;Y. J. Lee;G. Popescu;P. Bogdan
生长网络的多重分形特性
DOI：
--
发表时间：
2001
期刊：
影响因子：
--
作者：
S. Dorogovtsev;A. N. Samukhin;J. Mendes
通讯作者：
J. Mendes