## 6 Conclusions and Future Work  
Due to the difficulty of traditional methods in effectively handling the complex spatiotemporal information in LBSNs and the limitations imposed by high-dimensional redundancy and noise in the data, we propose a Heterogeneous Graph Attention Network (GEVEHGAN) based on Lite-GRU embedding and VAE enhancement for friend link prediction. This model integrates Skip-Gram and Lite-GRU to deeply mine spatiotemporal information in LBSNs and construct efficient feature representations. The VAE module plays a key role in dimensionality reduction, optimizing data dimensions and information representation, and reducing the interference of redundancy and noise on the model. The  
Figure 11: Analysis of the impact of loss function weight values on model performance metrics. (**a**) shows the trend of the AUC metric of the GEVEHGAN model with varying loss function weight values on the KL, JK, and IST datasets; (**b**) displays the trend of the model’s AP metric with varying loss function weight values on these three datasets.  
Figure 10: The impact of the number of attention heads in the multi-head attention mechanism on model performance metrics. (**a**) shows the trend of the AUC metric as the number of attention heads changes in the GEVEHGAN model on the KL, JK, and IST datasets; (**b**) shows the trend of the AP metric as the number of attention heads changes in these three datasets.  
introduction of edge-level attention mechanisms allows the model to focus precisely on the edge information in the heterogeneous graph that has a significant impact on friend link prediction, significantly improving the model's performance. Experimental results fully demonstrate the superiority of this model in key metrics such as accuracy and recall rate.  
However, the model still has some limitations. It does not explicitly model the multi-hop propagation mechanism of social influence, which may lead to a limited scope of recommendations. Additionally, it mainly focuses on location check-in behavior and does not cover other social network features such as text interactions or user comments, which may affect the model's generalization ability. Furthermore, privacy risks and data misuse are significant ethical concerns in LBSN tasks. When using LBSN data, privacy protection regulations should be followed, ensuring data de-identification and encryption to avoid unethical behaviors such as excessive commercialization or manipulation.  
Future research will explore more refined feature fusion strategies to fully leverage the multi-dimensional information in LBSNs. GEVEHGAN will be applied to practical scenarios such as personalized recommendation and social network influence analysis to verify its broad applicability, providing a new perspective for research in the LBSN field.  
In summary, the GEVEHGAN model has significant theoretical and practical value in friend link prediction within location-based social networks. We look forward to achieving more significant results in related fields through continuous optimization and expansion of the GEVEHGAN model.  
Conceptualization, Z.Y. and B.L.; methodology, Z.Y.; validation, Y.W.; formal analysis, Z.Y. and B.L.; investigation, Y.W.; resources, A.L.; data curation, Y.W.; writing--original draft preparation, Z.Y.; writing--review and editing, A.L.; visualization, Z.Y. and B.L.; supervision, A.L.; project administration, Z.Y. and Y.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The Foursquare dataset used in this study can be accessed at [[https://sites.google.com/site/yangdingqi/home/foursquare-dataset](https://sites.google.com/site/yangdingqi/home/foursquare-dataset)]([https://sites.google.com/site/yangdingqi/home/foursquare-dataset](https://sites.google.com/site/yangdingqi/home/foursquare-dataset)) (accessed on 15 August 2024).  
Thanks to [PERSON] for providing the GPU for model training.  
The authors declare no conflicts of interest.