Blanchard Philippe, Fortunato Santo, Krüger Tyll
Fakultät für Physik & BiBoS, Universität Bielefeld, D-33501 Bielefeld, Germany.
Phys Rev E Stat Nonlin Soft Matter Phys. 2005 May;71(5 Pt 2):056114. doi: 10.1103/PhysRevE.71.056114. Epub 2005 May 20.
The structure and the properties of complex networks essentially depend on the way nodes get connected to each other. We assume here that each node has a feature which attracts the others. We model the situation by assigning two numbers to each node, omega and alpha, where omega indicates some property of the node and alpha the affinity towards that property. A node A is more likely to establish a connection with a node B if B has a high value of omega and A has a high value of alpha. Simple computer simulations show that networks built according to this principle have a degree distribution with a power-law tail, whose exponent is determined only by the nodes with the largest value of the affinity alpha (the "extremists"). This means that the extremists lead the formation process of the network and manage to shape the final topology of the system. The latter phenomenon may have implications in the study of social networks and in epidemiology.
复杂网络的结构和属性本质上取决于节点相互连接的方式。我们在此假设每个节点都有一个吸引其他节点的特征。我们通过为每个节点分配两个数字,即ω和α来对这种情况进行建模,其中ω表示节点的某种属性,α表示对该属性的亲和性。如果节点B的ω值高且节点A的α值高,那么节点A更有可能与节点B建立连接。简单的计算机模拟表明,根据这一原理构建的网络具有幂律尾部的度分布,其指数仅由亲和性α值最大的节点(“极端分子”)决定。这意味着极端分子主导着网络的形成过程,并成功塑造了系统的最终拓扑结构。后一种现象可能在社交网络研究和流行病学研究中有重要意义。