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 global fluctuations is not yet well understood. A network may grow or shrink if a) the total population of nodes , including resting ones, changes and/or b) the chance of two nodes being connected varies over time. Here, we develop a method that classifies the source of global fluctuations of temporal networks according to these two mechanisms.We propose a dynamic hidden-variable model to formally define the two dynamical classes , with which we show that the global fluctuations in a real-world dynamical system can be explained by either of the two simple mechanisms. Our findings will contribute to a better understanding of the origin of the time-varying nature of complex networks .
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 .
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dynamical nature
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global fluctuations
time-varying nature
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according to these two mechanisms.We propose
. 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
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define
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, with which we show that the global fluctuations in a real-world dynamical system can be explained by either of the two simple mechanisms. Our findings will contribute to a better understanding of the origin of
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time-varying nature of complex networks
empirical data to identify whether the origin of scaling comes from a changing population in the system or shifts in the connecting probabilities