Official demo code for implementing the ReFactor GNN. We suggest you start with refactorgnn_demo_sgd_noreg.py, the very basic version of ReFactorGNN. Then go to refactorgnn_demo_sgd.py, which adds the N3 regularizer. Finally, check out the one induced by AdaGrad rather than SGD refactorgnn_demo_adagrad.py, which empirically perform better for muliti-relational link prediction tasks. Refer to our paper for more details.
If you find the code useful, please cite us by
@inproceedings{chen2022refactor,
title={ReFactor {GNN}s: Revisiting Factorisation-based Models from a Message-Passing Perspective},
author={Yihong Chen and Pushkar Mishra and Luca Franceschi and Pasquale Minervini and Pontus Stenetorp and Sebastian Riedel},
booktitle={Advances in Neural Information Processing Systems},
editor={Alice H. Oh and Alekh Agarwal and Danielle Belgrave and Kyunghyun Cho},
year={2022},
url={https://openreview.net/forum?id=81LQV4k7a7X}
}