Artificial Intelligence #artificial intelligence#graph neural networks
Boundary Embedding Shaping with Adaptive Contrastive Learning Boosts GNN Classification by 3.3%
Graph neural networks suffer from structural entanglement, especially near class boundaries. A new plug-in module called Boundary Embedding Shaping (BES) uses adaptive contrastive learning to selectively suppress spurious correlations, boosting GCN node classification by an average of 3.3% (up to 5% on WikiCS) and improving link prediction accuracy.
Jun 20, 2026 1 source