Abstract
Many temporal networks exhibit multiple system states, such as weekday and weekend patterns in social contact networks. The detection of such distinct states in temporal network data has recently been studied as it helps reveal underlying dynamical processes. A commonly used method is network aggregation over a time window, which aggregates a subsequence of multiple network snapshots into one static network. This method, however, necessarily discards temporal dynamics within the time window. Here we propose a new method for detecting dynamic states in temporal networks using connection series (i.e., time series of connection status) between nodes. Our method consists of the construction of connection series tensors over nonoverlapping time windows, similarity measurement between these tensors, and community detection in the similarity network of those time windows. Experiments with empirical temporal network data demonstrated that our method outperformed the conventional approach using simple network aggregation in revealing interpretable system states. In addition, our method allows users to analyze hierarchical temporal structures and to uncover dynamic states at different spatial/temporal resolutions.
Highlights
Temporal networks are a useful framework to represent and analyze time-dependent changes and underlying dynamics of complex systems [1,2,3]
We propose a method to detect dynamic states of temporal networks using connection series between nodes, i.e., the sequence of connection status between two nodes represented as a binary-valued vector (0: disconnected, 1: connected)
We developed a new method for detecting dynamic states in temporal networks
Summary
Temporal networks are a useful framework to represent and analyze time-dependent changes and underlying dynamics of complex systems [1,2,3]. System state detection is useful for investigating the dynamics of time-varying complex systems and making better interpretation of large-scale temporal network data sets. En a graph similarity was measured among the aggregated static networks to generate a distance matrix, to which hierarchical clustering was applied and the number of system states was determined using Dunn’s index [20] In their method, the timelines of interactions between nodes within a time window were aggregated as static edge weights. We propose a method to detect dynamic states of temporal networks using connection series between nodes, i.e., the sequence of connection status between two nodes represented as a binary-valued vector (0: disconnected, 1: connected). The connection series incorporates information regarding both amounts and temporal fluctuation of interactions between a pair of nodes, which may be more useful when detecting dynamic states of temporal networks.
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