HyperRegion: Integrating Graph and Hypergraph Contrastive Learning for Region Embeddings

Abstract

Region representations (also called embeddings) are useful for various urban computing tasks. While graph-based region representation learning methods have shown outstanding performance, they encounter two major challenges: 1) the pervasive data noise and missing data can affect the quality of the constructed region graphs; and 2) high-order relationships (i.e., group-wise relationships) among regions are often insufficiently modeled and sometimes entirely overlooked. To this end, we propose HyperRegion, an unsupervised region representation learning framework that integrates graph and hypergraph contrastive learning to learn comprehensive region embeddings from multi-modal data. Built upon a region hybrid graph network, this framework models both pair-wise and group-wise dependencies involving POI semantics, mobility patterns, geographic neighbors, and visual semantics. To mitigate the impact of data noise and missing data, graph and hypergraph contrastive learning are performed in parallel, and a cross-module contrast is further introduced to facilitate information exchange and collaboration. Extensive experiments on real-world datasets across three downstream tasks demonstrate that HyperRegion outperforms all baselines, particularly improving check-in prediction by reducing MAE and RMSE by approximately 8.5% and 8.2%, respectively, and increasing $R^{2}$ by about 7%.