Exploring Heterogeneous Information Networks via Pre-Training
TABLE IV: Experimental results on the multi-label node classification task.
['Model', 'DBLP MIC-F1', 'DBLP MAC-F1', 'YELP MIC-F1', 'YELP MAC-F1', 'YAGO MIC-F1', 'YAGO MAC-F1', 'Freebase MIC-F1', 'Freebase MAC-F1']
From which we could observe that GNN based models are the best baselines, showing the advancement of deep neural network on exploring the features of the network data for classification. Our PF-HIN still obtains the best experimental results thanks to our fine-tuning framework which aggregates the whole sequence information for node classification.