通过补全隐含知识，重构图结构以提升嵌入质量
Improving Graph Embeddings in Machine Learning Using Knowledge Completion with Validation in a Case Study on COVID-19 Spread
- 用衰减推理函数挖掘传递关系中的隐藏连接
- 改进后嵌入空间几何结构，显著提升表示效果
- 适合研究知识图谱补全与图神经网络优化的读者
图结构数据的兴起推动了图机器学习（GML）的发展，其中图嵌入（GEs）将知识图谱（KGs）的特征映射到向量空间，支持节点分类和链接预测等任务。然而，由于嵌入依赖显式拓扑和特征，可能遗漏看似稀疏数据中隐藏的隐含知识，影响图结构及表示效果。本文提出一种集成知识补全（KC）阶段的GML流程，在生成嵌入前揭示潜在语义。聚焦于传递关系，采用基于衰减的推理函数建模隐藏连接，重塑图拓扑，进而影响GraphSAGE与Node2Vec中的嵌入动态与聚合过程。实验表明，该流程显著改变嵌入空间的几何结构，证明其并非简单增强，而是重构图表示质量的变革性步骤。
原文摘要 · Abstract (English)
The rise of graph-structured data has driven major advances in Graph Machine Learning (GML), where graph embeddings (GEs) map features from Knowledge Graphs (KGs) into vector spaces, enabling tasks like node classification and link prediction. However, since GEs are derived from explicit topology and features, they may miss crucial implicit knowledge hidden in seemingly sparse datasets, affecting graph structure and their representation. We propose a GML pipeline that integrates a Knowledge Completion (KC) phase to uncover latent dataset semantics before embedding generation. Focusing on transitive relations, we model hidden connections with decay-based inference functions, reshaping graph topology, with consequences on embedding dynamics and aggregation processes in GraphSAGE and Node2Vec. Experiments show that our GML pipeline significantly alters the embedding space geometry, demonstrating that its introduction is not just a simple enrichment but a transformative step that redefines graph representation quality.
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