Exploring Heterogeneous Information Networks via Pre-Training
TABLE V: Experimental results on the node clustering task.
['Model', 'DBLP NMI', 'DBLP ARI', 'YELP NMI', 'YELP ARI', 'YAGO NMI', 'YAGO ARI', 'Freebase NMI', 'Freebase ARI']
According to this table, we find that despite the homogeneous GNN models’ strong ability of capturing the structural information of a network, they still perform slightly worse than those traditional heterogeneous models. And we attribute this to taking node type information into consideration, which could make a real difference when clustering the nodes. No doubt HetGNN is the best baseline combining both type information and deep neural networks. Our PF-HIN still outperforms all the baselines, proving that PF-HIN is able to generate effective node embeddings for node clustering.