Sha, HaoAl Hasan, MohammadMohler, George2023-02-232023-02-232021-10Sha, H., Al Hasan, M., & Mohler, G. (2021). Source detection on networks using spatial temporal graph convolutional networks. 2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA), 1–11. https://doi.org/10.1109/DSAA53316.2021.9564188https://hdl.handle.net/1805/31440Detecting 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.enPublisher Policysource detectionCOVID-19 epidemicspatial temporal graph convolutional networkArticle