摘要翻译：
我们讨论了非线性随机网络模型的复杂动力学，它是网络元素之间连通度k的函数。我们证明了这类网络在临界连通度k=2时表现出有序-混沌相变。同时，我们还证明了在动态临界网络中，两两相关性和复杂性度量都是最大的。这些结果与以往关于随机布尔网络和随机门限网络的研究一致，并再次表明临界网络提供了不同行为的最佳协调。
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英文标题：
《Phase transition in a class of non-linear random networks》
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作者：
M. Andrecut and S. A. Kauffman
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最新提交年份：
2010
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分类信息：

一级分类：Physics        物理学
二级分类：Adaptation and Self-Organizing Systems        自适应和自组织系统
分类描述：Adaptation, self-organizing systems, statistical physics, fluctuating systems, stochastic processes, interacting particle systems, machine learning
自适应，自组织系统，统计物理，波动系统，随机过程，相互作用粒子系统，机器学习
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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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英文摘要：
  We discuss the complex dynamics of a non-linear random networks model, as a function of the connectivity k between the elements of the network. We show that this class of networks exhibit an order-chaos phase transition for a critical connectivity k = 2. Also, we show that both, pairwise correlation and complexity measures are maximized in dynamically critical networks. These results are in good agreement with the previously reported studies on random Boolean networks and random threshold networks, and show once again that critical networks provide an optimal coordination of diverse behavior.
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PDF链接：
https://arxiv.org/pdf/1003.0871