Current - Issue

1. Bai, L., Yao, L., Li, C., Wang, X., & Wang, C. (2020). Adaptive graph convolutional recurrent network for traffic forecasting. Advances in
Neural Information Processing Systems (NeurIPS).
2. Chen, G., Chen, S., Li, D., & Chen, C. (2025). A hybrid deep learning air pollution prediction approach based on neighborhood selection
and spatio-temporal attention. Scientific Reports, 15, 3685.
3. Chen, J., Li, H., & Zhang, X. (2021). Spatio-temporal graph neural network for air quality prediction. Environmental Modelling & Software,
137.
4. Guo, S., Lin, Y., Feng, N., Song, C., & Wan, H. (2019). Attention based spatial-temporal graph convolutional networks for traffic flow
forecasting. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 922–929.
5. Li, Y., Yu, R., Shahabi, C., & Liu, Y. (2018). Diffusion convolutional recurrent neural network: Data-driven traffic forecasting.
International Conference on Learning Representations (ICLR).
6. Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., & Bengio, Y. (2018). Graph attention networks. International Conference
on Learning Representations (ICLR).
7. Wang, X., Zhang, S., Chen, Y., He, L., Ren, Y., Zhang, Z., Li, J., & Zhang, S. (2024). Air quality forecasting using a spatiotemporal hybrid
deep learning model based on VMD–GAT–BiLSTM. Scientific Reports, 14, 17841.
8. Yu, B., Yin, H., & Zhu, Z. (2018). Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting.
Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI), 3634–3640.
9. Zhang, Q., Wang, Y., & Li, Z. (2022). Multi-step air quality forecasting using graph neural networks with attention mechanism.
Atmospheric Environment, 286.
10. Zheng, C., Fan, X., Wang, C., & Qi, J. (2020). GMAN: A graph multi-attention network for traffic prediction. Proceedings of the AAAI
Conference on Artificial Intelligence, 34(01), 1234–1241.