Link prediction in knowledge graphs fundamentally depends on the quality of learned embeddings for entities and relations. However, most existing methods derive these embeddings by aggregating only the local neighborhood of each entity, neglecting the global structure of the knowledge graph. This limited view prevents models from capturing higher-level structural patterns that are essential for accurate and generalizable link prediction. To address these limitations, we introduce Model Graph Inductive Learning (\textbf{MGIL}), a framework that constructs a model graph by clustering entities based on the similarity of their incoming and outgoing relational structures or their entity types. A GNN is then applied to this model graph to produce embeddings that capture the global view of the knowledge graph. These embeddings subsequently serve as high-quality initial features %embeddings for the original knowledge graph, replacing random initialization and leading to more stable and expressive representations. Extensive experiments on standard and recently proposed inductive benchmarks demonstrate that MGIL achieves state-of-the-art or highly competitive performance in inductive link prediction, highlighting its effectiveness across diverse graph settings.

翻译：知识图谱中的链接预测从根本上依赖于学习到的实体和关系嵌入的质量。然而，现有方法大多仅通过聚合每个实体的局部邻域来推导这些嵌入，忽略了知识图谱的全局结构。这种受限的视角使得模型无法捕捉对于准确且可泛化的链接预测至关重要的高层结构模式。为解决这些局限，我们引入了模型图归纳学习（MGIL），该框架通过基于实体出入关系结构或实体类型的相似性对实体进行聚类，构建出一个模型图。随后，在该模型图上应用图神经网络生成能够捕捉知识图谱全局视角的嵌入。这些嵌入继而作为原始知识图谱的高质量初始特征，取代随机初始化，从而生成更稳定且更具表达力的表示。在标准及近期提出的归纳式基准上的大量实验表明，MGIL在归纳式链接预测任务中达到了最优或极具竞争力的性能，突显了其在不同图设置下的有效性。