## 3 KGs Learning
### Graph Neural Network (GNN)-Based Learning
#### 3.2.2 Link Prediction and Node Classification  
In Graph Neural Network (GNN)-based learning, link prediction and node classification are two key tasks that leverage the inherent structure of graphs. Link prediction infers missing or potential future connections by analyzing the relational patterns among nodes, while node classification assigns labels to nodes by considering both their individual features and the influence of their neighboring nodes. Together, these tasks empower various applications, such as recommendation systems, network analysis, and anomaly detection, by effectively capturing the underlying connectivity within the graph.  
By representing objects such as vehicles, pedestrians, and road facilities as a graph and combining it with a temporal LSTM, SG2 VEC [88] can predict future collision events. Node embeddings are directly optimized for the downstream task of \"collision likelihood\" and are lightweight for fast inference on actual AV (autonomous vehicle) edge hardware. MuCoMiD [103] addresses biological knowledge graphs (genes, miRNAs, diseases, proteins, etc.) using GNN-based multi-task learning. It addresses challenges such as data scarcity and noise by enabling automatic feature extraction and integrating more than five different biological data sources. The GNN layers learn the interactions between nodes (biomolecules), demonstrating robust predictions even with limited labeled data.  
The heterogeneous graph-based knowledge tracing method with spatiotemporal evolution (TSKT) [104] is designed to learn from graph data containing heterogeneous nodes and relationships. This model captures complex interactions among various node and relationship types through meta-paths and meta-graphs, achieving excellent performance in modeling learners' learning patterns. The Graph Structure Self-Contrasting (GSSC) [105] approach enhances graph-based learning performance by integrating graph structures into a multilayer perceptron (MLP). By incorporating graph structures into the input layer of the MLP, this model effectively learns the nonlinear interactions between nodes, significantly improving the node classification performance.  
The Graph-Aware Tensor Factorization Convolutional Network (GATFCN) [106] combines tensor factorization with graph convolution (GCN). By integrating graph structural information into the tensor factorization process, this model learns more precise interactions between nodes and relationships, achieving outstanding performance in tasks like link prediction. Ref. [107] combined a GNN with self-supervised learning to detect adverse effects in drug\(-\)drug interactions. This model learns complex relationships from large-scale drug datasets and captures patterns of drug\(-\)drug similarities, interactions, and side effects. It predicts potential side effects based on real clinical data, aiding in drug safety evaluation and clinical trial design. GraphX-Net [108], a GNN model designed for cancer survival prediction, represents patient data as nodes and similarities as edges to construct the graph structure. This model evaluates the contribution of each sample (patient) within the graph and accurately predicts the probability of survival in patients with cancer. By leveraging geometric features and graph structural information, advanced cancer survival predictions are supported, significantly contributing to personalized treatment design and survival analysis.