Learning from Counterfactual Links for Link Prediction

This repository contains the source code for the ICML 2022 paper:

Learning from Counterfactual Links for Link Prediction

by Tong Zhao ([email protected]), Gang Liu, Daheng Wang, Wenhao Yu, and Meng Jiang.

Requirements

This code package was developed and tested with Python 3.8.5, PyTorch 1.6.0, and PyG 1.6.1. All dependencies specified in the requirements.txt file. The packages can be installed by

pip install -r requirements.txt

Usage

The step of finding all the counterfactual links can be slow for the first run, please adjust the --n_workers parameter according to available processes if you are trying out different settings. The cached files for the counterfactual links that were used in our experiments can be found here, please download and put them under data/T_files/ before reproducing our experiments.

Following are the commands to reproduce our experiment results on different datasets.

# Cora
python main.py --dataset cora --metric auc --alpha 1 --beta 1 --gamma 30 --lr 0.1 --embraw mvgrl --t kcore --neg_rate 50 --jk_mode mean --trail 20

# CiteSeer
python main.py --dataset citeseer --metric auc --alpha 1 --beta 1 --gamma 30 --lr=0.1 --embraw dgi --t kcore --neg_rate 50 --jk_mode mean --trail 20

# PubMed
python main.py --dataset pubmed --metric auc --alpha 1 --beta 1 --gamma 30 --lr 0.1 --embraw mvgrl --t kcore --neg_rate 40 --jk_mode mean --batch_size 12000 --epochs 200 --patience 50 --trail 20

# Facebook
python main.py --dataset facebook --metric hits@20 --alpha 1e-3 --beta 1e-3 --gamma 30 --lr 0.005 --embraw mvgrl --t louvain --neg_rate 1 --jk_mode mean --trail 20

# OGBL-ddi
python main.py --dataset ogbl-ddi --metric hits@20 --alpha 1e-3 --beta 1e-3 --gamma 10 --lr 0.01 --embraw dgi --t louvain  --neg_rate 1 --jk_mode mean --epochs=200 --epochs_ft=200 --patience=50 --trail 20

Cite

If you find this repository useful in your research, please cite our paper:

@inproceedings{zhao2022learning,
  title={Learning from Counterfactual Links for Link Prediction},
  author={Zhao, Tong and Liu, Gang and Wang, Daheng and Yu, Wenhao and Jiang, Meng},
  booktitle={International Conference on Machine Learning},
  pages={26911--26926},
  year={2022},
  organization={PMLR}
}