论文标题
使用连接系列张量检测时间网络的动态状态
Detecting Dynamic States of Temporal Networks Using Connection Series Tensors
论文作者
论文摘要
许多时间网络都表现出多种系统状态，例如社交联系网络中的工作日和周末模式。最近探索了时间网络数据中这种不同状态的检测，因为它有助于揭示基本的动态过程。一种常用的方法是在一个时间窗口上进行网络聚合，该网络聚集将多个网络快照的子序列汇总到一个静态网络中。但是，此方法必然丢弃时间窗口内的时间动态。在这里，我们使用有关每对节点之间接触时间表的信息开发了一种用于检测时间网络中动态状态的新方法。我们采用通过处理时间序列和社区检测技术告知的相似性度量，将给定的时间网络依次将其分配到多个动态状态（包括重复）。经验时间网络数据的实验表明，我们的方法在揭示可解释的系统状态时使用简单的网络聚合优于常规方法。此外，我们的方法允许用户分析分层时间结构并在不同的空间/时间分辨率下发现动态状态。
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.
