Graph former-CL: A novel graph transformer with contrastive learning framework for enhanced drug-drug interaction prediction
Fig 6
Component contribution analysis and ablation study results.
The left panel shows performance degradation when removing individual components, with the full model achieving 98.20% accuracy. The right panel ranks component importance by performance drop when removed, identifying Cross-Modal Fusion (−1.28%) and Contrastive Learning (−0.86%) as critical components. The analysis reveals that components work together beyond individual contributions, achieving +1.12% total synergy when all components are combined.