DSTAGNN: Dynamic Spatial-Temporal Aware Graph Neural Network for Traffic Flow Forecasting

By Shiyong Lan, Yitong Ma, Wei Huang, Wenwu Wang, Hongyu Yang, Pyang Li. International Conference on Machine Learning, 2022

463 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/71423c75a47c1caf4d5ac8548b2f076c99a9da95

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Ming Jin
2. Junbo Zhang
3. Yuxuan Liang
4. Guangyin Jin
5. Zezhi Shao
6. Yu Zheng
7. Huan Yee Koh
8. Qingsong Wen

Citations in the corpus

• Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey (2023, background): “Feature initialization-based adaptive graph learning methods perform adaptive graph structure learning based on the given inputs or the hidden states [82], [113], [121], [127], [174]–[177].”
• A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection (2023, background): “For example, AST-GCN [86] and DSTAGNN [88] concurrently employ temporal attention and convolution in learning temporal correlations.”