Exploring Heterogeneous Information Networks via Pre-Training
TABLE VI: Computation cost on different tasks.
['[EMPTY]', 'LR', 'SS', 'MC', 'NC']
To evaluate the efficiency of our fine-tuning framework comparing other models, here we conduct the computation cost analysis. In specific, we first extract a subset of DBLP used for downstream task, i.e., 15% of the original DBLP dataset. Then in this subset, we analyze the running time of each model in each task, using the early stopping mechanism. For simplicity consideration, here we use LP, SS, MC and NC to represent link prediction, similarity search, multi-label node classification and node clustering tasks, respectively. The models are all conducted on GPU GTX-1080. running time is only a bit longer than those three traditional homogeneous models, DeepWalk, LINE and node2vec which are based on random walks. GNN based models like GCN, GraphSAGE, GAT and HetGNN cost much more time than other models, since the complexity of traditional deep neural networks is much higher than other algorithms. Although PF-HIN employs the bi-directional transformer encoders which is a variant of GNN, it is much more efficient than the above GNN based models. This is because that pre-trained parameters and embeddings could help the cost function converge much faster.