The experiments are conducted on the ogbn-arxiv and ogbn-mag datasets on the Stanford OGB (1.3.2) benchmark. The description of "Label-Enhanced Graph Neural Network for Semi-supervised Node Classification" is available here.
We provide the preprocessed datasets at here,
which should be put in the ./dataset
folder.
- run
./train_LEGNN/train_full_batch_LEGNN.py
to get the results of LEGNN on ogbn-arxiv. - run
./train_LEGNN_ASTrain/train_full_batch_LEGNN_ASTrain.py
to get the results of LEGNN + AS-Train on ogbn-arxiv.
- in addition to downloading the preprocessed datasets, you could also run
./preprocess_data/preprocess_ogbn_mag.py
to preprocess the original ogbn-mag dataset.
- run
./train_LEGNN/train_mini_batch_RGNN.py
to get the results of LEGNN on ogbn-mag. - run
./train_LEGNN_ASTrain/train_mini_batch_LEGNN_ASTrain.py
to get the results of LEGNN + AS-Train on ogbn-mag.
Model | Test Accuracy | Valid Accuracy | # Parameter | Hardware |
---|---|---|---|---|
LEGNN | 0.7337 ± 0.0007 | 0.7480 ± 0.0009 | 5,374,120 | NVIDIA Tesla T4 (15 GB) |
LEGNN + AS-Train | 0.7371 ± 0.0011 | 0.7494 ± 0.0008 | 5,374,120 | NVIDIA Tesla T4 (15 GB) |
Model | Test Accuracy | Valid Accuracy | # Parameter | Hardware |
---|---|---|---|---|
LEGNN | 0.5276 ± 0.0014 | 0.5443 ± 0.0009 | 5,147,997 | NVIDIA Tesla T4 (15 GB) |
LEGNN + AS-Train | 0.5378 ± 0.0016 | 0.5528 ± 0.0013 | 5,147,997 | NVIDIA Tesla T4 (15 GB) |
Please consider citing our paper when using the codes.
@article{yu2022label,
title={Label-Enhanced Graph Neural Network for Semi-supervised Node Classification},
author={Yu, Le and Sun, Leilei and Du, Bowen and Zhu, Tongyu and Lv, Weifeng},
journal={arXiv preprint arXiv:2205.15653},
year={2022}
}