Exploring Heterogeneous Information Networks via Pre-Training
TABLE X: Ablation analysis over Ranking-Based BFS Sampling Strategy.
['Model', 'LR AUC', 'LR F1', 'SS AUC', 'MC MIC-F1', 'MC MAC-F1', 'NC NMI', 'NC ARI']
PF-HIN +BFS outperforms PF-HIN +random, which is because that aggregating the node’s closest neighborhood’s information is more expressive than randomly chosen neighboring nodes. PF-HIN still outperforms PF-HIN +BFS, which indicates that choosing the nodes with higher importance is better for describing the features of a HIN.