摘要翻译：
复杂网络是普遍的，出现在社会学、物理学和生物学等不同的领域。在过去的十年里，对复杂系统的性质和行为的广泛研究发现了不同系统拓扑结构之间惊人的共性。解释这些相似性的尝试导致了网络模型和图论分析技术的不断发展和完善，用来表征和理解复杂性。在本教程中，我们通过说明性的例子来演示网络度量和模型如何有助于阐明复杂系统的组织。
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英文标题：
《Using graph concepts to understand the organization of complex systems》
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作者：
Claire Christensen and Reka Albert
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最新提交年份：
2006
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分类信息：

一级分类：Quantitative Biology        数量生物学
二级分类：Other Quantitative Biology        其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
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一级分类：Physics        物理学
二级分类：Disordered Systems and Neural Networks        无序系统与神经网络
分类描述：Glasses and spin glasses; properties of random, aperiodic and quasiperiodic systems; transport in disordered media; localization; phenomena mediated by defects and disorder; neural networks
眼镜和旋转眼镜；随机、非周期和准周期系统的性质；无序介质中的传输；本地化；由缺陷和无序介导的现象；神经网络
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一级分类：Quantitative Biology        数量生物学
二级分类：Molecular Networks        分子网络
分类描述：Gene regulation, signal transduction, proteomics, metabolomics, gene and enzymatic networks
基因调控、信号转导、蛋白质组学、代谢组学、基因和酶网络
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英文摘要：
  Complex networks are universal, arising in fields as disparate as sociology, physics, and biology. In the past decade, extensive research into the properties and behaviors of complex systems has uncovered surprising commonalities among the topologies of different systems. Attempts to explain these similarities have led to the ongoing development and refinement of network models and graph-theoretical analysis techniques with which to characterize and understand complexity. In this tutorial, we demonstrate through illustrative examples, how network measures and models have contributed to the elucidation of the organization of complex systems.
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PDF链接：
https://arxiv.org/pdf/q-bio/0609036