Hard Latent-Table Adaptation From Link Prediction to Subgraph Tasks

Link-prediction pretraining supplies node representations for subgraph classification. Adapting these representations, however, requires subgraph-level labels to update a node table shared by overlapping examples. These labels constrain aggregated predictions, leaving the individual node updates only indirectly supervised. We propose HardLT, a latent-table adaptation objective that couples complete-view task fitting with supervised training on globally coherent partial views. It constructs these views by masking fixed PCA directions jointly across all nodes before aggregation, then selects one common mask using the minibatch’s mean classification loss. The complete view fits the task, while the selected view supplies an additional constraint on adaptation. Dataset-specific configurations also align the two predictions through a consistency loss. With the GPEN encoder, HardLT achieves the highest mean scores on HPO-Metab, EM-User, and HPO-Neuro among the compared configurations, including a tie with GPEN on HPO-Neuro. In matched component studies, HardLT raises mean micro-F1 from 0.634 to 0.647 on HPO-Metab and from 0.916 to 0.922 on EM-User. Global sharing gives higher means than nodewise perturbations in both studies. Fixed-view evaluation on the same HPO-Metab checkpoints shows smaller performance decreases under shared latent-direction perturbations. HardLT adds no parameters or computation at inference.