seal_ogbl

SEAL Implementation for OGBL in DGL

Introduction

This is an example of implementing SEAL for link prediction in DGL. Some parts are migrated from https://github.com/facebookresearch/SEAL_OGB.

Requirements

PyTorch, DGL, OGB, and other python libraries: numpy, scipy, tqdm, sklearn, etc.

Usages

Run the following command for results on each benchmark

# ogbl-ppa
python main.py \
    --dataset ogbl-ppa \
    --use_feature \
    --use_edge_weight \
    --eval_steps 5 \
    --epochs 20 \
    --train_percent 5 

# ogbl-collab
python main.py \
    --dataset ogbl-collab \
    --train_percent 15 \
    --hidden_channels 256 \
    --use_valedges_as_input

# ogbl-ddi
python main.py \
    --dataset ogbl-ddi \
    --ratio_per_hop 0.2 \
    --use_edge_weight \
    --eval_steps 1 \
    --epochs 10 \
    --train_percent 5

# ogbl-citation2
python main.py \
    --dataset ogbl-citation2 \
    --use_feature \
    --use_edge_weight \
    --eval_steps 1 \
    --epochs 10 \
    --train_percent 2 \
    --val_percent 1 \
    --test_percent 1

Results

	ogbl-ppa (Hits@100)	ogbl-collab (Hits@50)	ogbl-ddi (Hits@20)	ogbl-citation2 (MRRd)
Paper Test Results	48.80%±3.16%	64.74%±0.43%	30.56%±3.86%*	87.67%±0.32r%
Our Test Results	49.48%±2.52%	64.23%±0.57%	27.93%±4.19%	86.29%±0.47%

* Note that the relatively large gap on ogbl-ddi may come from the high variance of results on this dataset. We get 28.77%±3.43% by only changing the sampling seed.

Reference

@article{zhang2021labeling,
    title={Labeling Trick: A Theory of Using Graph Neural Networks for Multi-Node Representation Learning},
    author={Zhang, Muhan and Li, Pan and Xia, Yinglong and Wang, Kai and Jin, Long},
    journal={Advances in Neural Information Processing Systems},
    volume={34},
    year={2021}
    }

@inproceedings{zhang2018link,
  title={Link prediction based on graph neural networks},
  author={Zhang, Muhan and Chen, Yixin},
  booktitle={Advances in Neural Information Processing Systems},
  pages={5165--5175},
  year={2018}
}