GRAPH NEURAL NETWORKS FOR LINK PREDICTION IN DYNAMIC KNOWLEDGE GRAPHS

Dynamic knowledge graphs, which capture evolving relationships among entities over time, are becoming increasingly important in various domains such as social networks, recommendation systems, finance domain and biomedical research. This research paper investigates the effectiveness of Graph Neural Networks (GNNs) for link prediction dynamic graphs. By leveraging temporal dynamics graph, we propose novel GNN architectures evaluate their performance against state-of- the-art methods on real-world datasets. The results demonstrate capability GNNs to effectively make accurate predictions providing valuable insights applications domains. Keywords—Graph Networks,Temporal Dynamics,Deep learning,DeepLearning,Link Prediction