Interpretable and Efficient Heterogeneous Graph Convolutional Network (ieHGCN)
Dataset Statics
Dataset |
# Nodes |
# Node Types |
# Edges |
# Edge Types |
Target |
# Classes |
DBLP |
26,128 |
4 |
239,566 |
6 |
author |
4 |
IMDB |
21,420 |
4 |
86,642 |
6 |
movie |
4 |
DBLP dataset refer to HGBDataset.
IMDBdataset refer to IMDB.
Performance
For the DBLP dataset: train test val = 974, 1420, 243 about 37% for training.
For the IMDB dataset: train test val = 400, 3478, 400, about 9% for training.
Dataset |
Paper(80% training) |
Paper(60% training) |
Paper(40% training) |
Paper(20% training) |
Our(tf) |
Our(th) |
Our(pd) |
DBLP |
96.29 |
95.25 |
93.83 |
93.85 |
92.30±0.49% |
90.90±0.74% |
91.18±0.66% |
IMDB |
58.35 |
60.84 |
59.81 |
56.60 |
58.10±0.42% |
55.22±1.21% |
56.08±2.13% |
TL_BACKEND="tensorflow" python3 iehgcn_trainer.py --dataset DBLP --n_epoch 30 --lr 0.01 --num_layers 3 --hidden_channels [64, 32] --l2_coef 0.0005 --drop_rate 0.2
TL_BACKEND="torch" python3 iehgcn_trainer.py --dataset DBLP --n_epoch 30 --lr 0.005 --num_layers 4 --hidden_channels [64, 32, 16] --l2_coef 0.0005 --drop_rate 0.0
TL_BACKEND="paddle" python3 iehgcn_trainer.py --dataset DBLP --n_epoch 30 --lr 0.01 --num_layers 4 --hidden_channels [64, 32, 16] --l2_coef 0.0005 --drop_rate 0.1
TL_BACKEND="torch" python3 iehgcn_trainer.py --dataset IMDB --n_epoch 25 --lr 0.01 --num_layers 3 --hidden_channels [64, 32] --l2_coef 0.0005 --drop_rate 0.2
TL_BACKEND="tensorflow" python3 iehgcn_trainer.py --dataset IMDB --n_epoch 25 --lr 0.005 --num_layers 3 --hidden_channels [64, 32] --l2_coef 0.0005 --drop_rate 0.2
TL_BACKEND="paddle" python3 iehgcn_trainer.py --dataset IMDB --n_epoch 25 --lr 0.005 --num_layers 3 --hidden_channels [64, 32] --l2_coef 0.0005 --drop_rate 0.2