Exploring Heterogeneous Information Networks via Pre-Training
TABLE VII: Ablation analysis over pre-training tasks.
['Model', 'LR AUC', 'LR F1', 'SS AUC', 'MC MIC-F1', 'MC MAC-F1', 'NC NMI', 'NC ARI']
We have the following observations. The MNM task plays a more important role for pre-training the model, as the performance drops dramatically after removing MNM. PF-HIN-ANP is slightly worse than PF-HIN on node classification and clustering tasks, while the gap is much larger on link prediction and similarity search tasks. This is because that ANP has a larger influence on fine-tuning framework with sequence pairs, which models the relationships between nodes.