TY - GEN
T1 - Cola-GNN
T2 - 29th ACM International Conference on Information and Knowledge Management, CIKM 2020
AU - Deng, Songgaojun
AU - Wang, Shusen
AU - Rangwala, Huzefa
AU - Wang, Lijing
AU - Ning, Yue
N1 - Publisher Copyright:
© 2020 ACM.
PY - 2020/10/19
Y1 - 2020/10/19
N2 - Forecasting influenza-like illness (ILI) is of prime importance to epidemiologists and health-care providers. Early prediction of epidemic outbreaks plays a pivotal role in disease intervention and control. Most existing work has either limited long-term prediction performance or fails to capture spatio-temporal dependencies in data. In this paper, we design a cross-location attention based graph neural network (Cola-GNN) for learning time series embeddings in long-term ILI predictions. We propose a graph message passing framework to combine graph structures (e.g., geolocations) and time-series features (e.g., temporal sequences) in a dynamic propagation process. We compare the proposed method with state-of-the-art statistical approaches and deep learning models. We conducted a set of extensive experiments on real-world epidemic-related datasets from the United States and Japan. The proposed method demonstrated strong predictive performance and leads to interpretable results for long-term epidemic predictions.
AB - Forecasting influenza-like illness (ILI) is of prime importance to epidemiologists and health-care providers. Early prediction of epidemic outbreaks plays a pivotal role in disease intervention and control. Most existing work has either limited long-term prediction performance or fails to capture spatio-temporal dependencies in data. In this paper, we design a cross-location attention based graph neural network (Cola-GNN) for learning time series embeddings in long-term ILI predictions. We propose a graph message passing framework to combine graph structures (e.g., geolocations) and time-series features (e.g., temporal sequences) in a dynamic propagation process. We compare the proposed method with state-of-the-art statistical approaches and deep learning models. We conducted a set of extensive experiments on real-world epidemic-related datasets from the United States and Japan. The proposed method demonstrated strong predictive performance and leads to interpretable results for long-term epidemic predictions.
KW - ILI prediction
KW - dynamic graph neural network
KW - spatial attention
UR - http://www.scopus.com/inward/record.url?scp=85095864146&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85095864146&partnerID=8YFLogxK
U2 - 10.1145/3340531.3411975
DO - 10.1145/3340531.3411975
M3 - Conference contribution
AN - SCOPUS:85095864146
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 245
EP - 254
BT - CIKM 2020 - Proceedings of the 29th ACM International Conference on Information and Knowledge Management
Y2 - 19 October 2020 through 23 October 2020
ER -