Source detection on networks using spatial temporal graph convolutional networks
Source detection on networks using spatial temporal graph convolutional networks
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DOI：
10.1109/dsaa53316.2021.9564188
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发表时间：
2021-10
期刊：
影响因子：
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通讯作者：
Hao Sha;Mohammad Al Hasan;George O. Mohler
中科院分区：
文献类型：
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作者：
Hao Sha;Mohammad Al Hasan;George O. Mohler
在像COVID-19这样的大流行期间检测爆发集群的来源可以深入了解传播过程、相关风险因素，并有助于遏制传播。在这项工作中，我们研究的问题，从多个快照的任意网络结构上传播的源检测。我们使用时空图卷积网络模型（SD-STGCN），通过融合时间和拓扑空间的信息来产生源概率分布。我们使用流行的房室模拟模型在合成网络和经验接触网络进行了广泛的实验。我们还用真实的COVID-19病例数据证明了我们方法的适用性。
Detecting the source of an outbreak cluster during a pandemic like COVID-19 can provide insights into the transmission process, associated risk factors, and help contain the spread. In this work we study the problem of source detection from multiple snapshots of spreading on an arbitrary network structure. We use a spatial temporal graph convolutional network based model (SD-STGCN) to produce a source probability distribution, by fusing information from temporal and topological spaces. We perform extensive experiments using popular compartmental simulation models over synthetic networks and empirical contact networks. We also demonstrate the applicability of our approach with real COVID-19 case data.