## 5 Experimental Setup
### Comparative Experiments
From the perspective of heterogeneous graph creation, GEVEHGAN combines two types of nodes--users and POIs--within the heterogeneous graph and explicitly models the multi-dimensional interactions of social, spatiotemporal, and semantic aspects through three relationships: friend edges, check-in edges, and co-occurrence edges. Unlike traditional homogeneous graph models (such as GCN, GAT, and GraphSAGE), GEVEHGAN provides a more comprehensive reflection of user behavior characteristics through multi-type edge fusion. Compared with Metapath2 vec, Heter-GCN, and MAGNN, GEVEHGAN avoids the loss of semantic information and simplifies the model by directly modeling edge attributes (such as time and geographical distance). When compared with AllSetTransformer, GEVEHGAN is more precise in handling fine-grained user-POI interactions in LBSNs and can accurately distinguish edge semantics. From the perspective of node embedding and dimensionality reduction denoising, GEVEHGAN significantly improves node embedding quality through multimodal embedding joint modeling and generative denoising. Compared with traditional models that rely on neighborhood aggregation (such as GCN, GAT, and GraphSAGE), GEVEHGAN reduces noise and captures the temporal dependencies in POI co-occurrence semantics and user trajectories. Through probabilistic compression of high-dimensional embeddings using VAE, GEVEHGAN effectively filters random check-in noise. Compared with Metapath2 vec and Heter-GCN, GEVEHGAN's Lite-GRU can capture short-term behavior sequences and enhance robustness through VAE, mitigating interference from sparse data. In comparison with MAGNN and AllSetTransformer, GEVEHGAN balances spatiotemporal continuity and semantic correlation, better adapting to the dynamic nature of user behavior in LBSNs, providing high-discriminatory low-noise embeddings for downstream tasks. From the perspective of information aggregation and propagation, GEVEHGAN significantly enhances information aggregation and propagation through its edge-level attention mechanism and residual-enhanced heterogeneous information fusion architecture. Compared with traditional homogeneous models (such as GCN, GAT, and GraphSAGE), GEVEHGAN avoids edge-type semantic confusion caused by node-level attention and uniform aggregation, preserving key spatiotemporal behavioral information. It dynamically computes the weights of the three types of edges and combines multi-head attention with residual connections, improving edge semantic modeling accuracy. Compared with static heterogeneous models (such as Metapath2 vec and Heter-GCN), GEVEHGAN flexibly handles the diversity of user-POI interactions in LBSNs and captures complex behavior patterns. Compared with more complex heterogeneous frameworks (such as MAGNN and AllSetTransformer), GEVEHGAN avoids noise introduction and over-smoothing issues by dynamically adjusting the intensity of information propagation and using residual connections. The precise-edge semantic modeling and multi-dimensional attention capture the synergistic influence of spatiotemporal, social, and interest factors, satisfying the heterogeneity and dynamics requirements of LBSN data. Overall, heterogeneous graph methods are better at mining and utilizing the richer semantic and topological information in heterogeneous social networks, compared with homogenous graph methods that rely solely on topological information in social networks. The GEVEHGAN model proposed in this chapter, compared with previous heterogeneous graph methods, does not require manually designed metapaths but learns node representations directly from the entire heterogeneous graph. It not only considers the high-dimensional noise in LBSN data and the temporal features of user mobility trajectories but also takes into account the importance of different types of nodes and edges. These features enable the GEVEHGAN model to achieve better results in friend link prediction tasks.