Weisfeiler and Leman Go Relational (LOG 2022)
MIT License
This is the official code base of the paper
Weisfeiler and Leman Go Relational
Pablo Barcelo, Mikhail Galkin, Christopher Morris, Miguel Romero Orth
This repo contains the code for reproducing the experiments on R-GCN and CompGCN with the one-hot feature initialization strategy.
Notice on the k-RN architecture: We plan to update the repo with the k-RN implementation as soon as we come up with the meaningful relational dataset to evaluate k-RNs.
The experiments were performed on Python 3.8.
Dependencies:
torch 1.10.0
torch-cluster 1.5.9
torch-geometric 2.0.2
torch-scatter 2.0.9
torch-sparse 0.6.12
Optionally, install wandb
for results tracking, prepend WANDB_ENTITY=yourentity
to the running script and use the --wandb
flag.
python main.py --dataset AIFB --lr 0.001 --epochs 8001 --rgcn_fast --drop_bias --dim 4
python main.py --dataset AIFB --lr 0.001 --epochs 8001 --rgcn_fast --mod_rgcn --drop_bias --dim 4
python main.py --dataset AIFB --lr 0.001 --epochs 8001 --compgcn --dim 4
python main.py --dataset AIFB --lr 0.001 --epochs 8001 --compgcn --dim 4 --compgcn_no_dir
python main.py --dataset AIFB --lr 0.001 --epochs 8001 --compgcn --dim 4 --compgcn_no_relupd
python main.py --dataset AIFB --lr 0.001 --epochs 8001 --compgcn --dim 4 --no_norm
You can combine those flags for CompGCN as well.
Experiments on the big AM dataset are forced on a CPU due to the dataset size.
Options for --msg_func
: transe
, distmult
, rotate
Options for --aggr_func
: add
, mean
Please refer to the Appendix F in the paper for the full set of hyperparameters.
If you find this project useful in your research, please cite the following paper
@inproceedings{
barcelo2022weisfeiler,
title={Weisfeiler and Leman Go Relational},
author={Pablo Barcelo and Mikhail Galkin and Christopher Morris and Miguel Romero Orth},
booktitle={Learning on Graphs Conference},
year={2022},
url={https://openreview.net/forum?id=wY_IYhh6pqj}
}