Exploring Heterogeneous Information Networks via Pre-Training
TABLE III: Experimental results on the similarity search task.
['[EMPTY]', 'DBLP', 'YELP', 'YAGO', 'Freebase']
The best results are highlighted in bold. According to this table, we could observe that traditional heterogeneous models and homogeneous GNN models achieve comparable outputs, which means that both meta-path based mechanism and deep neural networks can generate expressive node embeddings for similarity search. HetGNN is still the best baseline which proves the power of the combination of GNN and type features. PF-HIN performs the best in all cases, illustrating the effectiveness of our pre-training and fine-tuning framework on learning the node representations for similarity search.