Improving Graph Embeddings in Machine Learning Using Knowledge Completion with Validation in a Case Study on COVID-19 Spread

2025-11-15

AI Summary

Existing graph embedding methods overly rely on explicit topological structures, limiting their ability to capture implicit semantic and transitive relationships in sparse graphs. To address this, we propose a novel knowledge-augmented graph embedding paradigm: for the first time, we integrate a decay-based logical inference mechanism for knowledge completion into the graph preprocessing stage, enabling automatic discovery and strengthening of implicit edges and dynamic topology reconstruction. Subsequently, we combine GraphSAGE and Node2Vec to generate high-quality node representations. Our approach significantly improves the geometric structure and semantic coherence of the embedding space. Extensive experiments on multiple benchmark datasets demonstrate substantial gains in node classification and link prediction performance. These results empirically validate the critical importance of modeling implicit knowledge for graph representation learning.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Knowledge Representation and Reasoning: Knowledge Acquisition

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

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.

Problem

• Addresses implicit knowledge gaps in graph embeddings from sparse datasets
• Proposes knowledge completion to uncover latent semantics before embedding generation
• Transforms graph topology and representation quality through inference functions

Innovation

• Knowledge completion uncovers latent semantics before embedding
• Decay-based inference models hidden connections with transitive relations
• Reshaping graph topology alters embedding space geometry significantly