中图分类号：P41
文献标识码：A
DOI：10.19358/j.issn.2096-5133.2021.04.006
引用格式： 陈杰，许镇义. 基于高阶图卷积网络的城市空气质量推断模型[J].信息技术与网络安全，2021，40(4)：33-41，45.

A high-order graph convolutional network for urban air quality inference

Abstract：Whether it can accurately predict the air quality distribution is of great significance to the government′s environmental governance and people′s daily health prevention. This problem is challenging for the following reasons：(1)The air quality distribution in different regions has temporal and spatial interaction；(2)The air quality distribution is affected by external factors. In recent years，generalized convolutional neural network(CNN) is one of the research hotspots to process arbitrary graph structured data, so the fine-grained air quality forecasting problem in urban areas is formulated as an urban spatio-temporal graph prediction problem.Based on the proposed high-order graph convolution, we design an effective air quality inference model for inferring the air quality distribution, which could capture the spatio-temporal interaction of air quality distribution and extract external influential factor features. Through the verification of Beijing air quality data, experimental results show that proposed approach far outperforms known baseline methods.

Key words :air quality；spatial-temporal interaction；graph convolutional network；semi-supervised learning