论文标题
热门富丽堂网络增长模型
Hot-Get-Richer Network Growth Model
论文作者
论文摘要
在优先依恋（PA）网络增长模型下，延迟到达在其最终学位方面处于不利地位。 PA的先前扩展已通过将节点健身的概念添加到PA（通常是从某些健身分数分布中）或单独使用健身来控制附件来解决这种缺陷。在这里，我们引入了一种新的动力学方法，通过向PA添加最近的度量变化偏置来解决延迟到达，从而使与到达节点的时间接近相对程度变化较高的节点获得了附件概率的增强。换句话说，如果PA描述了一种丰富的机制，并且基于健身的方法描述了良好的机制，那么我们的模型可以将其描述为一种热门的毛刺机制，在这种机制中，热度是由最近的一些过去的学位变化确定的。所提出的模型比PA模型要生产的高级节点要晚，在某些参数下，产生的网络具有类似于PA网络的结构。
Under preferential attachment (PA) network growth models late arrivals are at a disadvantage with regard to their final degrees. Previous extensions of PA have addressed this deficiency by either adding the notion of node fitness to PA, usually drawn from some fitness score distributions, or by using fitness alone to control attachment. Here we introduce a new dynamical approach to address late arrivals by adding a recent-degree-change bias to PA so that nodes with higher relative degree change in temporal proximity to an arriving node get an attachment probability boost. In other words, if PA describes a rich-get-richer mechanism, and fitness-based approaches describe good-get-richer mechanisms, then our model can be characterized as a hot-get-richer mechanism, where hotness is determined by the rate of degree change over some recent past. The proposed model produces much later high-ranking nodes than the PA model and, under certain parameters, produces networks with structure similar to PA networks.
