How Embeddings Shape Graph Neural Networks: Classical vs Quantum-Oriented Node Representations
Researchers benchmark node embedding strategies for graph neural networks, comparing classical baselines against quantum-oriented representations under controlled conditions across five TU datasets and QM9. The study isolates embedding impact by standardizing backbone architecture, data splits, optimization, and evaluation metrics.
MentionsGraph Neural Networks · Quantum-oriented embeddings · TU datasets · QM9
Read full story at arXiv cs.LG →(arxiv.org)
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