UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction | Paper Citation Record

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UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction Unresolved cited work

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This paper cites Bikesharing.

UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction Bikesharing

Reference 6

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UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction Unresolved cited work

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UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction Unresolved cited work

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This paper cites Multi-Modality Spatio-Temporal Forecasting via Self-Supervised Learning.

UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction Multi-Modality Spatio-Temporal Forecasting via Self-Supervised Learning

Reference 10

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Reference 13

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UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction Unresolved cited work

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UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction Unresolved cited work

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UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction Unresolved cited work

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UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction Unresolved cited work

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UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction Unresolved cited work

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UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction Unresolved cited work

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UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction Unresolved cited work

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This paper cites Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting.

UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting

Reference 30

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UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction Bikesharing

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Reference 42

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UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction Jensen, Zhenli Sheng, and Bin Yang

Reference 44

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Reference 46

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This paper cites Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting.

UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting

Reference 49

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Reference 50

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UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction Conformalized Selective Regression

Reference 51

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Reference 52

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UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction Graph WaveNet for Deep Spatial-Temporal Graph Modeling

Reference 57

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This paper cites an unresolved cited work.

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UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting

Reference 61

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Reference 63

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UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction Unresolved cited work

Reference 66

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Reference 67

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Reference 68

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Observation 712b0bdd-8f12-4422-826c-4f36e323dcaf · outbound

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UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction Unresolved cited work

Reference 69

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UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction Unresolved cited work

Reference 70

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Observation 954300eb-3e07-4622-8981-e763d648a099 · outbound

This paper cites an unresolved cited work.

UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction Unresolved cited work

Reference 77

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Observation 15fdb387-06c3-4eda-b3a6-ed5796f8cc04 · outbound

This paper cites In proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.

UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction In proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

Reference 2017

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Observation cf1b42aa-74e1-48ca-bc48-641358fb70c7 · outbound

This paper cites In Neural Information Processing: 25th International Conference, ICONIP 2018, Siem Reap, Cambodia, December 13-16, 2018, Proceedings, Part I 25.

UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction In Neural Information Processing: 25th International Conference, ICONIP 2018, Siem Reap, Cambodia, December 13-16, 2018, Proceedings, Part I 25

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:41:24.055353Z

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Observation 05de6adf-b1c0-4859-b688-b783bbd632bf · outbound

This paper cites In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining.

UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:41:23.953482Z

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Observation 2419ec17-ee34-43b2-bc15-61b0ba5db4c9 · outbound

This paper cites Proceedings of the National Academy of Sciences 119, 7 (2022), e2111870119.

UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction Proceedings of the National Academy of Sciences 119, 7 (2022), e2111870119

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:41:24.511703Z

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Observation 15e5821f-c987-4ead-befe-91c9cbfc4997 · outbound

This paper cites Proceedings of the VLDB Endowment 17, 4 (2023), 753–765.

UQGNN: Uncertainty Quantification of Graph Neural Networks for Multivariate Spatiotemporal Prediction Proceedings of the VLDB Endowment 17, 4 (2023), 753–765

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T17:41:23.834100Z

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Outbound references

Pith citing papers