Abstract
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.
Highlights
Dynamical behaviors of various complex systems can be described by temporal patterns of interactions among constituents of the systems, which have recently been studied in the framework of temporal networks [1,2,3]
We explore the effects of environmental changes in the dynamics of temporal networks through data analysis and network modeling
Based on the empirical results, we propose a temporal network model to study the role of environmental changes in the dynamics of complex social systems
Summary
Dynamical behaviors of various complex systems can be described by temporal patterns of interactions among constituents of the systems, which have recently been studied in the framework of temporal networks [1,2,3]. Despite the importance of such external factors in understanding the temporal correlations observed in temporal networks, we find only few studies on the effects of external factors on bursty temporal interaction patterns These effects have been studied, e.g., by modeling circadian and weekly patterns with a periodic event rate or activity level [33, 34] or by de-seasoning the cyclic behaviors from the bursty time series [35]. We first analyze the several temporal network datasets, some of which are known to be affected by the time-varying external factors, by means of the multiscale entropy (MSE) method [49, 50] for detecting temporal correlations in multiple timescales. Our modeling approach helps us better understand how the environmental changes may affect the non-trivial temporal interaction patterns observed in the empirical temporal networks
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