CB-SAGE: A novel centrality based graph neural network for floor plan classification

Abstract

Graph Neural Networks (GNNs) have emerged as one of the most prominent research areas in accomplishing machine learning tasks over graphical networks. GNNs are prominently used in performing tasks like semi-supervised node classification, link prediction, and community detection. GraphSAGE is one of the most recent GNN models which is being used to accomplish these tasks. A floor plan is an architectural design of a building that represents various floor compartments. In this paper, we represent floor plan(s) as graph(s). We characterize floor room (compartment) classification as a node classification problem. We propose a variation of the traditional GraphSAGE (sample and aggregation) algorithm: Centrality based GraphSAGE (CB-SAGE), which captures the structural properties of the network. We use the average clustering coefficient and average betweenness centrality to capture structure properties. We compute CB scores for all the floor plan graphs. Top 70% nodes (based on CB scores) are selected for the training. During the training, we append the betweenness centrality score of each node as an additional feature in the feature matrix for the embedding process. We conduct experiments on the House-GAN dataset, which contains 1,43,184 vectorized floor plan images. The proposed method outperforms the current state-of-art models in accomplishing the task of floor plan classification. We compare our results with the traditional machine learning approach (MLP) and other GNN-based methods. Our approach achieves an accuracy of 96.70%, which is significantly (approximately 16%) higher than other state-of-the-art methods.