Exploring Heterogeneous Information Networks via Pre-Training
TABLE II: Experimental results on the link prediction task.
['Model', 'DBLP AUC', 'DBLP F1', 'YELP AUC', 'YELP F1', 'YAGO AUC', 'YAGO F1', 'Freebase AUC', 'Freebase F1']
From it we could observe that the outputs become better with a decreasing scale of the datasets. Traditional homogeneous models like DeepWalk, LINE and node2vec perform worse than traditional heterogeneous meta-path based models metapath2vec, HINE and HIN2Vec, which indicates that meta-path captures the network structure better than random walks. However, homogeneous GNN models like GCN, GraphSAGE and GAT have even better outputs comparing traditional heterogeneous methods. We attribute this to the fact that deep neural network explores the whole network in a better way, generating better representations for link prediction. HetGNN outperforms those homogeneous GNN models, since it takes the node types into consideration. Our model PF-HIN outperforms all the baselines consistently and significantly, which verifies that our fine-tuning framework based on bi-directional transformers is effective on modeling the relationships between nodes, so as to predict the links in between.