论文标题
时间网络发展中两种类型的致密缩放
Two types of densification scaling in the evolution of temporal networks
论文作者
论文摘要
随着时间的流逝，许多现实世界中的社交网络不断地改变其全球属性，例如边缘，大小和密度的数量。尽管已经对社交网络的时间和局部特性进行了广泛的研究，但其动力学性质的起源尚未得到充分理解。如果a）节点的总群体变化和/或b）网络可能会增长或收缩，并且/或b）两个节点被连接的机会随时间而变化。在这里，我们开发了一种方法，使我们能够对时间网络的时变性质进行分类。在这样做时，我们首先显示了现实世界动力学系统可以分为两个类别的经验证据，其差异的特征是边缘数量随活动节点的数量增长的方式，即致密化缩放。我们开发一个动态的隐藏变量模型，以正式表征两个动态类。该模型拟合到经验数据中，以确定缩放的起源是系统中的人群不断变化还是连接概率的变化。
Many real-world social networks constantly change their global properties over time, such as the number of edges, size and density. While temporal and local properties of social networks have been extensively studied, the origin of their dynamical nature is not yet well understood. Networks may grow or shrink if a) the total population of nodes changes and/or b) the chance of two nodes being connected varies over time. Here, we develop a method that allows us to classify the source of time-varying nature of temporal networks. In doing so, we first show empirical evidence that real-world dynamical systems could be categorized into two classes, the difference of which is characterized by the way the number of edges grows with the number of active nodes, i.e., densification scaling. We develop a dynamic hidden-variable model to formally characterize the two dynamical classes. The model is fitted to the empirical data to identify whether the origin of scaling comes from a changing population in the system or shifts in the connecting probabilities.
