Two types of densification scaling in the evolution of temporal networks
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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.
収録刊行物
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- Physical Review E
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Physical Review E 102 (5), 052302-052302, 2020-11-09
American Physical Society (APS)
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詳細情報 詳細情報について
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- CRID
- 1050012570391880320
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- NII論文ID
- 120006940245
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- NII書誌ID
- AA11558033
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- ISSN
- 24700053
- 24700045
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- HANDLE
- 20.500.14094/90007673
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- 本文言語コード
- en
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- 資料種別
- journal article
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- データソース種別
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