## 1 Introduction  
With the widespread adoption of location-based social networks (LBSNs), massive quantities of check-in data have been generated continuously, revealing complex patterns of human mobility at different scales [1, 2]. These check-in records not only contain basic geographical coordinates and timestamps but also encode rich hierarchical information, from individual venue preferences to district-level activities and city-wide movement patterns. This multi-scale nature of human mobility data provides unprecedented opportunities for understanding user behaviors at different granularities [3]. Recent advances in deep learning have shown remarkable success in modeling complex spatio-temporal dependencies in various real-world scenarios, such as high-speed railroad scene parsing from UAV imagery [4] and fast vehicle detection in traffic environments [5]. These studies demonstrate the potential of deep models to extract structured representations from rich spatial and temporal cues. However, effectively modeling hierarchical relationships in human mobility data for accurate next point of interest (POI) prediction, as illustrated in Figure 1, remains a significant challenge due to the need to capture both fine-grained location transitions and coarse-grained mobility patterns. Early research in POI recommendation primarily focused on matrix factorization approaches [3; 6; 7; 8; 9; 10], which established foundational methods by integrating user preferences with geographical influence. However, these models often struggle to capture sequential patterns and may not perform well in sparse data scenarios, which can limit their effectiveness in dynamic environments. Building on this foundation, sequential models [11; 12; 13; 14; 15; 16; 17] have been developed to leverage temporal patterns using advanced neural networks and attention mechanisms [18] to enhance predictive performance. While models such as Recurrent Neural Networks (RNNs) [19] and Long Short-Term Memory (LSTM) [20] networks are adept at capturing temporal dependencies, they still struggle with long-term dependencies and lack global collaborative information, which can lead to performance degradation over time when facing new data. More recently, graph-based models [21; 22; 23; 24; 25; 26] have emerged as a powerful approach to capturing complex dependencies and relationships. These models utilize graph structures to achieve superior results in POI recommendation by modeling the inherent relationships between users, locations, and check-in sequences. However, in the effort to enhance POI recommendation accuracy, two key challenges emerge: the hierarchical nature of human mobility and data sparsity. Firstly, human movement patterns naturally exhibit a hierarchical structure--from individual venue preferences to district-level activities and city-wide movement patterns. Traditional models that treat all POIs at a single granularity fail to capture these multi-scale relationships, leading to suboptimal recommendations. Secondly, the sparsity of check-in data further complicates the modeling of these hierarchical patterns. Users' check-ins are often unevenly distributed and concentrated around familiar locations, making it difficult to establish reliable connections between different geographical scales. While existing approaches have made progress in addressing data sparsity, they typically construct single-layer graphs that cannot effectively model the inherent hierarchical relationships in human mobility data. To address these limitations, we propose a novel hierarchical graph learning framework that models POI relationships at multiple scales. Specifically, we construct a three-level graph structure: a base-level graph capturing direct POI transitions, a region-level graph modeling area-based interactions through spatio-temporal clustering, and a global-level graph representing category-based patterns.