Effects of topology on network evolution
Effects of topology on network evolution
复制标题
DOI：
10.1038/nphys359
复制
发表时间：
2006-08-01
期刊：
影响因子：
19.6
通讯作者：
Cluzel, Philippe
中科院分区：
文献类型：
--
作者：
Oikonomou, Panos;Cluzel, Philippe
自然界中无处不在的无尺度拓扑提出了这样一个问题：这种特殊的网络设计是否具有进化优势(1)。一系列研究已经确定了控制无标度网络的增长和动力学的关键原理(2-4)。在这里，我们使用基于神经元的布尔组件网络作为框架，对自然和人工系统中的一大类动态行为进行建模(5-7)。应用训练算法，我们描述了具有不同拓扑的网络如何通过随机突变和选择的过程向预先建立的目标函数进化(8-10)。我们发现，齐次随机网络和无标度网络呈现出截然不同的演化路径。齐次随机网络累积中性突变并以稀疏间隔步长(11，12)进化，而无标度网络快速且连续进化。值得注意的是，后一种性质对度指数的变化是稳健的。相比之下，同构随机网络需要对其连接性进行特定调整，以优化其进化能力。这些结果突出了一个组织原则，该原则支配着复杂网络的演变，并可以改进工程系统的设计。
The ubiquity of scale-free topology in nature raises the question of whether this particular network design confers an evolutionary advantage(1). A series of studies has identified key principles controlling the growth and the dynamics of scale-free networks(2-4). Here, we use neuron-based networks of boolean components as a framework for modelling a large class of dynamical behaviours in both natural and artificial systems(5-7). Applying a training algorithm, we characterize how networks with distinct topologies evolve towards a pre-established target function through a process of random mutations and selection(8-10). We find that homogeneous random networks and scale-free networks exhibit drastically different evolutionary paths. Whereas homogeneous random networks accumulate neutral mutations and evolve by sparse punctuated steps(11,12), scale-free networks evolve rapidly and continuously. Remarkably, this latter property is robust to variations of the degree exponent. In contrast, homogeneous random networks require a specific tuning of their connectivity to optimize their ability to evolve. These results highlight an organizing principle that governs the evolution of complex networks and that can improve the design of engineered systems.