Information Theory of Complex Networks: On Evolution and Architectural Constraints
Information Theory of Complex Networks: On Evolution and Architectural Constraints
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DOI：
10.1007/978-3-540-44485-5_9
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发表时间：
2004
期刊：
影响因子：
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通讯作者：
R. Solé;S. Valverde
中科院分区：
文献类型：
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作者：
R. Solé;S. Valverde
摘要复杂网络的特点是高度异构分布的链接，往往弥漫的关键属性，如鲁棒性下节点删除的存在。已经定义了几个相关措施，以表征这些网络的结构。在这里，我们表明，互信息，噪声和联合熵可以正确地定义在一个静态图。这些措施计算了一些真实的网络和一些简单的标准模型的分析估计。它表明，真实的网络聚集在一个定义良好的域的熵噪声空间。通过使用模拟退火优化，它表明，最优异构网络实际上集群在同一个狭窄的域，这表明强约束实际上操作的复杂网络的可能的宇宙。进化的影响进行了讨论。
AbstractComplex networks are characterized by highly heterogeneous distributions of links, often pervading the presence of key properties such as robustness under node removal. Several correlation measures have been defined in order to characterize the structure of these nets. Here we show that mutual information, noise and joint entropies can be properly defined on a static graph. These measures are computed for a number of real networks and analytically estimated for some simple standard models. It is shown that real networks are clustered in a well-defined domain of the entropy-noise space. By using simulated annealing optimization, it is shown that optimally heterogeneous nets actually cluster around the same narrow domain, suggesting that strong constraints actually operate on the possible universe of complex networks. The evolutionary implications are discussed.