A Hybrid Supervised and Self-Supervised Graph Neural Network for Edge-Centric Applications
This work addresses edge-centric tasks—such as protein–protein interaction prediction and similarity assessment of structurally unknown compounds—by proposing a graph neural network framework that unifies supervised and self-supervised learning. Methodologically, it is the first to integrate both loss terms into a unified edge-level prediction objective; introduces an edge-aware attention mechanism that jointly models node and edge features; and incorporates self-supervised contrastive learning with a lightweight feed-forward prediction head, enabling end-to-end edge representation learning using only one-hot node features. Key contributions include: (1) resolving the long-standing challenge of similarity prediction for compounds lacking 3D structural information; and (2) achieving state-of-the-art performance on both protein–protein interaction prediction and gene ontology functional annotation tasks.