Asia Pacific Center for Theoretical Physics, Pohang, Republic of Korea.
Department of Physics, The Catholic University of Korea, Bucheon, Republic of Korea.
PLoS One. 2021 Apr 28;16(4):e0250612. doi: 10.1371/journal.pone.0250612. eCollection 2021.
Dynamics of complex social systems has often been described in the framework of temporal networks, where links are considered to exist only at the moment of interaction between nodes. Such interaction patterns are not only driven by internal interaction mechanisms, but also affected by environmental changes. To investigate the impact of the environmental changes on the dynamics of temporal networks, we analyze several face-to-face interaction datasets using the multiscale entropy (MSE) method to find that the observed temporal correlations can be categorized according to the environmental similarity of datasets such as classes and break times in schools. By devising and studying a temporal network model considering a periodically changing environment as well as a preferential activation mechanism, we numerically show that our model could successfully reproduce various empirical results by the MSE method in terms of multiscale temporal correlations. Our results demonstrate that the environmental changes can play an important role in shaping the dynamics of temporal networks when the interactions between nodes are influenced by the environment of the systems.
复杂社会系统的动态经常在时间网络的框架中进行描述，其中链接仅被认为在节点之间的相互作用的时刻存在。这种相互作用模式不仅受到内部相互作用机制的驱动，而且受到环境变化的影响。为了研究环境变化对时间网络动态的影响，我们使用多尺度熵 (MSE) 方法分析了几个面对面的交互数据集，发现可以根据数据集的环境相似性（例如学校的班级和休息时间）对观察到的时间相关性进行分类。通过设计并研究一个考虑周期性变化环境以及优先激活机制的时间网络模型，我们通过数值模拟表明，我们的模型可以通过 MSE 方法成功地再现各种经验结果，在多尺度时间相关性方面。我们的结果表明，当节点之间的相互作用受到系统环境的影响时，环境变化可以在时间网络的动态中发挥重要作用。
