WeightedHGE: Weighted Heterogeneous Graph Embedding

In this paper, we introduce a novel method for embedding weighted graphs, designed to capture the significance of relationships in real-world weighted graph data. Unlike existing models that treat all relationships equally, our approach incorporates edge weights directly into the embedding process, enhancing the representation of highly weighted connections. By introducing modifications to both the scoring and loss functions, our method emphasizes the importance of weighted relationships during training. Theoretical analysis shows that our model maintains efficiency and scalability while significantly improving the capacity to represent weighted graph structures. Experimental results demonstrate that our approach consistently outperforms baseline methods, including TransE, in tasks involving weighted relationships, showcasing its robustness and applicability.