A Deep Graph Learning-Enhanced Assessment Method for Industry-Sustainability Coupling Degree in Smart Cities

Abstract

The construction of smart cities has been a common long-term goal around the world. In addition to fundamental infrastructures, it also remains important to assess healthy development status of cities with use of intelligent algorithms. Currently, machine learning has gradually been the prevalent technical means to develop digital assessment methods. However, the whole social system can be regarded as a kind of graph-level complex network, in which node entities and their internal relations are involved. To deal with this challenge, this paper takes graph-level feature into consideration, and proposes a deep graph learning-enhanced assessment method for industry-sustainability coupling degree in smart cities. Specifically, an improved graph neural network model is developed to output the industry space aggregation consequence, and a multi-variant regression model is utilized to output the sustainability status level consequence. Taking the Guangdong-Hong Kong-Macau Greater Bay Area (GBA) as an example, simulative experiments are carried out on the real-world data collected from realistic society. The obtained results can well prove that the proposed method is able to effectively assess the industry-sustainability coupling degree in smart cities.