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

2 citations · 5 across the 6 of their papers we have counts for

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cs.LG2025

Bi-View Embedding Fusion: A Hybrid Learning Approach for Knowledge Graph's Nodes Classification Addressing Problems with Limited Data

Rosario Napoli, Giovanni Lonia, Antonio Celesti +2

Traditional Machine Learning (ML) methods require large amounts of data to perform well, limiting their applicability in sparse or incomplete scenarios and forcing the usage of add…

cs.LG2025★ 2 cited

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

Rosario Napoli, Gabriele Morabito, Antonio Celesti +2

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 space…