Residual2Vec: Debiasing graph embedding using random graphs

This repository contains the code for

• S. Kojaku, J. Yoon, I. Constantino, and Y.-Y. Ahn, Residual2Vec: Debiasing graph embedding using random graphs. NerurIPS (2021). [link will be added when available]

• Preprint (arXiv): https://arxiv.org/abs/2110.07654

• BibTex entry:

@inproceedings{kojaku2021neurips,
 title={Residual2Vec: Debiasing graph embedding using random graphs},
 author={Sadamori Kojaku and Jisung Yoon and Isabel Constantino and Yong-Yeol Ahn},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {},
 pages = {},
 publisher = {Curran Associates, Inc.},
 volume = {},
 year = {2021}
}

Installation and Usage of residual2vec package

pip install residual2vec

The code and instruction for residual2vec sits in libs/residual2vec. See here.

Reproducing the results

We set up Snakemake workflow to reproduce our results. To this end, install snakemake and run

snakemake --cores <# of cores available> all

which will produce all figures for the link prediction and community detection benchmarks. The results for the case study are not generated due to the limitation by our data sharing aggreements.