Detecting Dynamic States of Temporal Networks Using Connection Series Tensors

TL;DR

This paper introduces a novel method for detecting dynamic states in temporal networks by analyzing contact timelines, outperforming traditional aggregation methods and enabling hierarchical and multi-resolution analysis.

Contribution

The paper presents a new approach that leverages contact timelines and similarity measures for dynamic state detection, capturing hierarchical and multi-scale temporal structures.

Findings

Outperforms traditional network aggregation in revealing system states

Enables analysis of hierarchical temporal structures

Uncovers dynamic states at different spatial and temporal resolutions

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 explored 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 develop a new method for detecting dynamic states in temporal networks using information regarding the timeline of contacts between each pair of nodes. We apply a similarity measure informed by the techniques of processing time series and community detection to sequentially discompose a given temporal network into multiple dynamic states (including repeated ones). 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 state at different spatial/temporal resolutions.