There are various dynamic networks around us. Many researchers have investigated the fitness, i.e. ability to get edges from other nodes, and popularity effects on network growth. The fitness-popularity dynamic network (FPDN) model was introduced recently. In the FPDN model, the fitness of a node is assumed invariant for a given period of time. In many real networks, however, the fitness may change over time in various ways. Herein, we propose a varying fitness-popularity dynamic network (V-FPDN) model by allowing variable fitness. Through the V-FPDN model, we can estimate the strength of fitness and popularity effects and show how the fitness of the nodes changes. The magnitude of these effects and fitness values are estimated simultaneously using the expectation-maximization (EM) algorithm combined with the Markov chain Monte Carlo (MCMC) method. We apply the FPDN and V-FPDN model to the Facebook wallpost network and compare the results. The YouTube subscription network is inv...

中文翻译：

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关于适应度变化的分析：适应度变化的适应度-人口动态网络模型

我们周围有各种各样的动态网络。许多研究人员已经研究了适应性，即从其他节点获取边缘的能力以及流行度对网络增长的影响。最近引入了适应性人口动态网络（FPDN）模型。在FPDN模型中，假定节点的适应性在给定的时间段内不变。但是，在许多实际网络中，适应性可能会随着时间以各种方式变化。在本文中，我们通过允许变量适应度提出了一种适应度-大众度动态网络（V-FPDN）模型。通过V-FPDN模型，我们可以估算适应度和受欢迎程度的强度，并显示节点适应度如何变化。使用结合马尔可夫链蒙特卡洛（MCMC）方法的期望最大化（EM）算法，可以同时估算这些影响的大小和适用性值。我们将FPDN和V-FPDN模型应用于Facebook wallpost网络并比较结果。YouTube订阅网络已开始...