<ul><li>Researchers present a method based on graph neural networks (GNN) to predict usage probabilities of shopping-mall corridors.</li><li>The method utilizes heterogeneous graph networks created from floorplans of malls, including corridors, shops, and entrances.</li><li>Features such as shop area and usage categories are used for prediction, along with connections represented as corridor paths.</li><li>The method incorporates a supervised deep-learning workflow, including latent feature representation learning and multilayer perceptrons (MLP) for final predictions.</li></ul>

Prediction of Usage Probabilities of Shopping-Mall Corridors Using Heterogeneous Graph Neural Networks

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