Two types of densification scaling in the evolution of temporal networks
Two types of densification scaling in the evolution of temporal networks
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DOI：
10.1103/physreve.102.052302
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发表时间：
2020-11-09
影响因子：
2.4
通讯作者：
Genois, Mathieu
中科院分区：
文献类型：
--
作者：
Kobayashi, Teruyoshi;Genois, Mathieu
随着时间的推移，许多现实世界中的社交网络不断地改变它们的全局属性，例如边数、大小和密度。虽然社会网络的时态和局部性已经被广泛研究，但其动态性质的起源还没有被很好地理解。如果(A)节点总数改变和/或(B)两个节点被连接的机会随时间变化，则网络可能增长或缩小。在这里，我们开发了一种方法，允许我们对时变网络的来源进行分类。在此过程中，我们首先给出了现实世界动力系统可以分为两类的经验证据，这两类系统的不同之处在于边的数量随着活跃节点的数量的增加而增长，即致密化标度。我们建立了一个动态隐变量模型来形式化地刻画这两个动态类。将该模型与经验数据进行拟合，以确定标度的起源是来自系统中不断变化的人口，还是来自于连接概率的变化。
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.