MaskPOI: A POI Representation Learning Method Using Graph Mask Modeling
Haoyuan Zhang, Zexi Shi, Mei Li, Shanjun Mao · Electronics · 2025
Point of Interest (POI) data play a critical role in enabling location-based services (LBS) by providing intrinsic attributes, including geographic coordinates and semantic categories, alongside a spatial context that reflects relationships among POIs. However, the inherent label sparsity in POI datasets poses significant challenges for traditional supervised learning approaches. To address this limitation, we propose MaskPOI, a novel self-supervised learning framework that combines the strengths of graph neural networks and masked modeling. MaskPOI incorporates two complementary modules: an edge mask-based graph autoencoder that models the spatial topology by predicting edge existence and uncovering hidden spatial relationships and a feature mask-based graph autoencoder that reconstructs masked node features to explore the rich attribute characteristics of POIs. Together, these modules enable MaskPOI to jointly capture the spatial and attribute information essential for robust representation learning. Extensive experiments demonstrate MaskPOI’s effectiveness in improving performance on downstream tasks such as functional zone classification and population density prediction. Ablation studies further validate the contributions of its components, highlighting MaskPOI as a powerful and versatile framework for POI representation learning.