网络进化中的相互选择：内在适应度的作用
Mutual selection in network evolution: the role of the intrinsic fitness
摘要：
我们提出了一种新的机制，导致无标度网络，这是基于存在的一个固有的字符的顶点称为健身。在我们的模型中，一个顶点$i$被分配一个健身$x_i$，从一个给定的概率分布函数$f(x)$。在网络演化过程中，使用速率$p$，我们添加适应度为$x_j$的顶点$j$，并将其连接到优先选择的适应度为$x_i$的现有顶点$i$，连接概率函数$g(x_i,x_j)$取决于所涉及的两个顶点的适应度，并且使用速率$1-p$，我们在两个已经存在的顶点之间创建具有适应度$x_i$和$x_j$的边，其概率也优先于连接功能$g(x_i,x_j)$。对于适当的选择$g$，所得到的网络具有广义幂律，而不考虑顶点的适应度分布。
英文摘要：
We propose a new mechanism leading to scale-free networks which is based on the presence of an intrinsic character of a vertex called fitness. In our model, a vertex $i$ is assigned a fitness $x_i$, drawn from a given probability distribution function $f(x)$. During network evolution, with rate $p$ we add a vertex $j$ of fitness $x_j$ and connect to an existing vertex $i$ of fitness $x_i$ selected preferentially to a linking probability function $g(x_i,x_j)$ which depends on the fitnesses of the two vertices involved and, with rate $1-p$ we create an edge between two already existed vertices with fitnesses $x_i$ and $x_j$, with a probability also preferential to the connection function $g(x_i,x_j)$. For the proper choice of $g$, the resulting networks have generalized power laws, irrespective of the fitness distribution of vertices.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
