Air quality prediction is a typical spatio-temporal modeling problem, which always uses different components to handle spatial and temporal dependencies in complex systems separately. Previous models based on time series analysis and Recurrent Neural Network (RNN) methods have only modeled time series while ignoring spatial information. Previous GCNs-based methods usually require providing spatial correlation graph structure of observation sites in advance. The correlations among these sites and their strengths are usually calculated using prior information. However, due to the limitations of human cognition, limited prior information cannot reflect the real station-related structure or bring more effective information for accurate prediction. To this end, we propose a novel Dynamic Graph Neural Network with Adaptive Edge Attributes (DGN-AEA) on the message passing network, which generates the adaptive bidirected dynamic graph by learning the edge attributes as model parameters. Unlike prior information to establish edges, our method can obtain adaptive edge information through end-to-end training without any prior information. Thus reduced the complexity of the problem. Besides, the hidden structural information between the stations can be obtained as model by-products, which can help make some subsequent decision-making analyses. Experimental results show that our model received state-of-the-art performance than other baselines.

翻译：空气质量预测是一个典型的时空建模问题，现有方法通常采用不同组件分别处理复杂系统中的空间依赖和时间依赖。基于时间序列分析和循环神经网络（RNN）的方法仅对时间序列进行建模，而忽略了空间信息。以往基于图卷积网络（GCNs）的方法通常需要预先提供观测站点间的空间关联图结构，这些站点间的关联性及其强度通常通过先验信息计算得出。然而，受限于人类认知的局限性，有限的先验信息无法反映真实的站点关联结构，也难以提供更有效的信息以支持精准预测。为此，我们提出一种基于消息传递网络的新型动态图神经网络——自适应边属性动态图神经网络（DGN-AEA），该方法通过将边属性作为模型参数进行学习，生成自适应双向动态图。与传统依赖先验信息建立边的方式不同，我们的方法无需任何先验信息即可通过端到端训练获得自适应边信息，从而降低了问题的复杂度。此外，该模型可以以副产品形式获取站点间的隐藏结构信息，有助于后续决策分析。实验结果表明，我们的模型在性能上优于其他基线模型，达到了最优效果。