Detecting Dynamic States of Temporal Networks Using Connection Series Tensors
Detecting Dynamic States of Temporal Networks Using Connection Series Tensors
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DOI：
10.1155/2020/9649310
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发表时间：
2020-07
期刊：
影响因子：
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通讯作者：
Shun Cao;Hiroki Sayama
中科院分区：
文献类型：
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作者：
Shun Cao;Hiroki Sayama
许多时间网络表现出多个系统状态，如社会联系网络中的工作日和周末模式。在时间网络数据中检测这种不同的状态最近已经被研究，因为它有助于揭示潜在的动力学过程。一种常用的方法是在时间窗口上进行网络聚合，将多个网络快照的子序列聚合到一个静态网络中。然而，该方法必然丢弃时间窗口内的时间动态。在这里，我们提出了一种新的方法来检测动态状态的时间网络使用连接系列（即，连接状态的时间序列）。我们的方法包括在不重叠的时间窗口，这些张量之间的相似性测量，并在这些时间窗口的相似性网络的社区检测连接系列张量的建设。实验表明，我们的方法优于传统的方法，使用简单的网络聚合揭示可解释的系统状态。此外，我们的方法允许用户分析层次的时间结构，并发现在不同的空间/时间分辨率的动态状态。
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 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.