## 2 Related Work
### Link Prediction Method  
The task of link prediction is the process of predicting missing relationships between entity pairs within a graph structure. Link prediction methods can be classified into heuristic methods, matrix factorization methods, random walk methods, community detection methods, rule-based methods, feature learning methods, machine learning methods, and GNN methods. Based on heuristic methods, strategies using metaheuristics [22] are combined with the Particle Swarm Optimization (PSO) [23] approach and the topological properties of social network graphs to locate missing relationships in symbolic social networks. Ref. [24] analyzed the problem of link prediction in complex networks, elucidating the relationship between the degree of clustering of nodes in a network and node degree, and expounded on the existing link prediction methods and technologies as well as the application areas and challenges of heuristic methods. Based on matrix decomposition methods, the Sparse-Mult [25] model employs a matrix decomposition method that breaks down a large matrix into the product of several smaller matrices. This approach significantly reduces computational complexity, allowing the model to handle large-scale data more effectively. Based on random walk methods, the DeepWalk [26] algorithm simulates random walk paths between nodes purely by random walk, capturing the structure of the network and thereby addressing the problem of missing relationships in the network. [PERSON]. [27] summarized the impact of network embedding feature learning methods on link prediction tasks, such as DeepWalk, Large-scale Information Network Embedding (LINE), and node2 vec. Network embedding methods generate node embeddings by controlling the random walk process between nodes, thereby capturing the structure and relationships within the network. Based on community discovery methods, some existing approaches do not consider important information such as community features, text information, and growth mechanisms in link prediction tasks. Therefore, [PERSON]. [28] incorporated node features into community detection algorithms based on the characteristics of social networks, further improving the process of community discovery methods in link prediction tasks. Based on rule-based methods, the Anytime Bottom-Up Rule Learning (AnyBURL) [29] model performed well in various general link prediction tasks. However, its aggregation process is affected by the redundancy of multiple rules. Thus, the Scalable and Fast Non-redundant Rule Application (SAFRAN) [30] was used to detect cluster redundant rules before aggregation, thereby improving the effect of the AnyBURL model. Based on feature learning methods, in combat systems, diverse systems and information flows were treated as nodes and edges, using the Representation Learning based Heterogeneous Combat Network (RLHCN) [31] method to predict various link types in the combat network. Based on machine learning methods, in graph-structured data, the popularity of each node is computed and the similarity metric between each pair of nodes in the network is assessed. These two metrics constitute the features of the node pairs, and this feature data is input into a machine learning classifier [32] to identify missing relationships in the network. [PERSON]. [33] improved the water environment by combining machine learning and regional features of artificial immune algorithms to extract images of floating objects in polluted water sources, effectively detecting floating pollutants on the water surface and aiding in water resource management [34]. In medical image segmentation tasks, feature learning regression network models provided a better method for image segmentation, which can effectively solve the problem of retinal layer segmentation bias. Link prediction methods have evolved from simple heuristic algorithms to complex GNNs, with gradually improving performance. Heuristic algorithms are simple, but their performance is limited.