## 2 Related Work  
### Graph Neural Network  
In recent years, with the advancement of deep learning technologies, numerous new graph neural network structures have emerged. In the task of link prediction, traditional methods inferring missing links from a global topological perspective have consistently proven ineffective, leading to the development of various graph neural network approaches. The Graph Attention Network (GAT) [14] aggregates neighbor node information by learning the relationship weights between each node and its neighbors, achieving improved results in predicting missing links within various large-scale networks. The Graph Convolutional Recurrent Neural Network (GCRN) [15] combines the structures of convolutional and recurrent neural networks to model neighbor nodes temporally while considering global information. The Graph Variational Autoencoder (GVAE) [16], through mapping graph data to a latent space and introducing the concept of variational autoencoders, can generate new graph data, contributing to graph generation and reconstruction tasks. Additionally, the learning ability of the Graph Recursive Neural Network (GRNN) [17] is combined with the multi-scale characteristics of wavelet decomposition to perform more accurate field strength prediction, assisting in cellular network planning and performance optimization. Experimental results demonstrate the GRNN's robust capability to capture information in multiple stages. Although each of these structures can be selected according to specific tasks, they have their own disadvantages and limitations. For instance, certain structures might suffer from excessive computational complexity when handling large-scale graph data, experiencing insufficient capturing of complex graph patterns and long-distance dependencies, or a lack of adequate model interpretability. Overcoming these challenges necessitates the design and improvement of new structures. Therefore, in response to the limitations of existing graph neural network models, this paper proposes a method based on Graph Convolutional Networks (GCNs) [18] for tasks such as link prediction and node classification. GCNs aggregate local neighbor information through convolution operations on adjacency matrices. This enables the model to capture complex inter-node relationships while preserving graph structure information. It conducts weighted aggregation based on the features of neighboring nodes, allowing each node to effectively integrate information from its neighbors, thereby generating context-aware representations. However, GCNs only use first-order neighbor information for aggregation operations, preventing the model from fully capturing relationships between nodes and more distant neighbors. Moreover, during the aggregation process, all neighbor nodes are assigned the same weight, which means the model cannot differentiate the importance of different neighboring nodes. These limitations lead to some constraints in GCNs. Therefore, to address these issues, GraphSAGE has been proposed as an improvement on GCNs. In medical and pharmaceutical research, [PERSON]. [19] applied the GraphSAGE model to integrate multimodal and complex relationship biomedical networks, effectively predicting adverse drug interactions and impacts between drugs and their targets. This plays a significant role in constructing drug knowledge graphs for Coronavirus Disease 2019 (COVID-19). Experimental results show that the GraphSAGE model can effectively predict drug-disease interactions for COVID-19. In the realm of Internet of Things (IoT) intrusion detection systems, [PERSON]. [20] utilized the GraphSAGE model to capture edge features of graph structure data and topological information for network intrusion detection within IoT networks, conducting extensive experimental evaluations on four Network IntrusionDetection System (NIDS) benchmark datasets with positive results. In industrial Internet attack detection systems, [PERSON].