Extracting the hierarchical organization of complex systems
Extracting the hierarchical organization of complex systems
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DOI：
10.1073/pnas.0703740104
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发表时间：
2007-09-25
影响因子：
11.1
通讯作者：
Amaral, Luis A. Nunes
中科院分区：
文献类型：
--
作者：
Sales-Pardo, Marta;Guimera, Roger;Amaral, Luis A. Nunes
从不断增长的生物和社会经济数据“海洋”中提取理解是我们面临的最紧迫的科学挑战之一。在这里，我们介绍并验证了一种无监督的方法，用于提取复杂的生物，社会和技术网络的层次组织。我们定义了一个合奏的层次嵌套随机图，我们用它来验证的方法。然后，我们将我们的方法应用于现实世界的网络，包括航空运输网络，电子电路，电子邮件交换网络和代谢网络。我们的模型和真实的网络的分析表明，我们的方法提取一个复杂的系统的准确的多尺度表示。
Extracting understanding from the growing "sea" of biological and socioeconomic data is one of the most pressing scientific challenges facing us. Here, we introduce and validate an unsupervised method for extracting the hierarchical organization of complex biological, social, and technological networks. We define an ensemble of hierarchically nested random graphs, which we use to validate the method. We then apply our method to real-world networks, including the air-transportation network, an electronic circuit, an e-mail exchange network, and metabolic networks. Our analysis of model and real networks demonstrates that our method extracts an accurate multiscale representation of a complex system.