Representation and assessment of spatial design using a hierarchical graph neural network: Classification of shopping center types

Abstract

The assessment and prediction of spatial designs are essential in the building industry, where the primary challenge include finding a way to represent spatial designs and a numeric method to extract pattern. Herein, a shopping center type classification task was used to illustrate the application of the graph structure and hierarchical graph neural network (GNN) for the floor plan representation and classification. A graph dataset consisting of 359 s floor plans was established and used to train three different GNNs in two groups of experiments. The results showed that the test accuracies of the three GNNs were higher than those obtained with multinomial logistic regression and a support vector machine with Weisfeiler–Lehman kernel, and the differentiable pooling GNN with node function labels achieved the highest accuracy (70%). These findings indicate the advantages of GNNs, particularly the hierarchical pooling algorithm, and demonstrate their potential in spatial design embedding and assessment.