Heterogeneous information networks (HINs) refer to logical networks involving entities of multiple types and their multiple relations, which are widely used for modeling real-world systems with rich features and intricate patterns. Link prediction in such networks is a consistent interesting research question due to its methodological and practical implications in the business field. This study develops an improved spatial graph convolution network to learn predictive vertex embeddings with minimal information loss based on local community discovery and to handle the complexity of link predictions in the context of HINs. An optimizable kernel layer is designed to measure the similarity of pairwise vertex embeddings. The effectiveness of the proposed method is validated using four real-world HINs: WordNet, MovieLens, DBLP, and Douban. The results of the experiments demonstrate that the proposed method outperforms several benchmark algorithms, achieving F1-scores of 87.65%, 84.27%, 82.99%, and 89.96% on the four HINs, respectively. The findings of this study can inform the design and improvement of related information systems.

异构信息网络（HIN）是指涉及多种类型的实体及其多重关系的逻辑网络，广泛用于建模具有丰富功能和复杂模式的真实世界系统。由于这种方法在业务领域中的方法论和实践意义，因此在此类网络中的链接预测一直是有趣的研究热点。这项研究开发了一种改进的空间图卷积网络，以基于局部社区发现来学习具有最小信息损失的预测性顶点嵌入，并在HIN的上下文中处理链接预测的复杂性。一个可优化的内核层旨在测量成对顶点嵌入的相似性。使用四个实际的HIN验证了所提方法的有效性：WordNet，MovieLens，DBLP和Douban。实验结果表明，该方法优于几种基准算法，在四个HIN上的F1分数分别为87.65％，84.27％，82.99％和89.96％。这项研究的结果可以为相关信息系统的设计和改进提供参考。