Non-Negative Kernel Graphs for Time-Varying Signals Using Visibility Graphs
Non-Negative Kernel Graphs for Time-Varying Signals Using Visibility Graphs
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DOI：
10.23919/eusipco55093.2022.9909594
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发表时间：
2022-08
期刊：
影响因子：
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通讯作者：
Ecem Bozkurt;Antonio Ortega
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文献类型：
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作者：
Ecem Bozkurt;Antonio Ortega
提出了一种利用非负核(NNK)图结构将时变信号表示为动态图的新框架。我们扩展了原有的NNK框架，允许将显式延迟作为图构造的一部分，因此与NNK框架不同的是，如果两个节点中的一个移位后信号之间的相似度较高，则两个节点可以与对应于非零时间延迟的边连接。我们还提出了用不同节点上信号的可见性图的节点度和聚类系数来表征不同节点上信号之间的相似性。图的边可以表示时间延迟，我们提供了一个新的视角，使我们能够看到同步在时间序列信号的图构造中的效果。对于温度和脑电信号数据集，我们证明了我们的方法可以实现稀疏和可解释的图形表示。此外，所提出的方法对于利用稀疏性来刻画不同的脑电实验是有用的。
We present a novel framework to represent sets of time-varying signals as dynamic graphs using the non-negative kernel (NNK) graph construction. We extend the original NNK framework to allow explicit delays as part of the graph construction, so that unlike in NNK, two nodes can be connected with an edge corresponding to a non-zero time delay, if there is higher similarity between the signals after shifting one of them. We also propose to characterize the similarity between signals at different nodes using the node degree and clustering coefficients of their respective visibility graphs. Graph edges that can representing temporal delays, we provide a new perspective that enables us to see the effect of synchronization in graph construction for time-series signals. For both temperature and EEG datasets, we show that our proposed approach can achieve sparse and interpretable graph representations. Furthermore, the proposed method can be useful in characterizing different EEG experiments using sparsity.