## 1 Introduction  
With the rapid development of the internet, social platforms have built a bridge between the online virtual world and offline real life [1], becoming an indispensable part of modern society. Friend link prediction, as one of the core areas of social network research, plays a significant role in optimizing the user experience on social platforms. It also provides solid support for application scenarios such as targeted marketing [2], event promotion [3], and career development [4]. At the same time, accurate friend relationship prediction can help social platforms recommend new friends that align with users' interests, promoting interaction and communication, thereby effectively increasing user activity and platform value. Location-Based Social Networks, with their recorded user location activity information, provide new opportunities for friend link prediction [5]. Friend link prediction methods can be primarily divided into traditional methods and deep learning methods. The models based on similarity and probabilistic statistics can be classified as traditional methods. Similarity-based methods are the simplest approach in link prediction. They predict unobserved links by calculating the similarity score for each pair of nodes. This score is usually computed based on the structure or attributes of the nodes, with node pairs that have higher scores considered as potential links. Commonsimilarity methods include Common Neighbors (CNs) [6], Random Walk with Restart (RWR) [7], and Local Random Walk Index [8]. Probabilistic models [9; 10; 11] construct a model consisting of multiple parameters by optimizing the objective function. They use conditional probability to assess the likelihood of a link's existence, such as in probabilistic tensor factorization [12] and stochastic Markov models [13]. In addition to relying on structural information, probabilistic models also require additional edge attribute knowledge, which is often difficult to extract. Moreover, the applicability of these models is limited because their performance depends on accurate parameter tuning. Therefore, these models are less suited for handling large-scale networks. The complexity of LBSN data also makes traditional prediction methods less effective in practical applications. In recent years, deep learning technologies have continuously advanced, and researchers have proposed various deep learning-based methods to improve the effectiveness of link prediction. [PERSON] et al. [14] proposed the Conditional Temporal Restricted Boltzmann Machine (CTRBM) to capture the link structure in dynamic networks. [PERSON] et al. [15] conducted link prediction for static/dynamic networks based on structural features, but this method has performance limitations. To improve performance, [PERSON] et al. [16] designed a hierarchical Bayesian model that combines structural and node features. [PERSON] et al. [17] introduced the Relational Graph Convolutional Network (R-GCN), which represents relational data (such as knowledge graphs) as directed multi-graphs and uses Graph Convolutional Networks (GCNs) to model nodes and relationships, enabling node classification and link prediction. Meanwhile, [PERSON] et al. [18] proposed the Variational Graph Auto-Encoder (VGAE) framework, which learns latent representations of graph-structured data using GCN and reconstructs them through simple inner products. These methods, by combining techniques like Graph Neural Networks and generative models, are capable of more accurately capturing complex relationships in dynamic networks, thereby significantly improving the performance of node and link prediction. However, despite the opportunities that Graph Neural Networks (GNNs) [19] provide for more accurate and efficient friend link prediction, effectively extracting user features from vast and sparse information and integrating complex and diverse data has become a new challenge. Most GNN-based friend link prediction methods do not fully leverage the heterogeneous data of LBSNs.