Exploring Heterogeneous Information Networks via Pre-Training
TABLE IX: Ablation analysis over input representation.
['Model', 'LR AUC', 'LR F1', 'SS AUC', 'MC MIC-F1', 'MC MAC-F1', 'NC NMI', 'NC ARI']
Type and rank embeddings could help improve the model performance as PF-HIN obtains the best results on all tasks. Offering the model with a sense of type information has a large influence on the performance, since removing it deteriorates the results a lot, especially on node clustering task. This is because that node clustering task is more sensitive to the type information. Removing rank embeddings also hurts the performance, since the ranking information could help assign higher weights on important nodes in a HIN. No doubt that the model PF-HIN-type-rank achieves the worst performance.