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FairDrop

This is the companion code for the paper:

Spinelli I, Scardapane S, Hussain A, Uncini A, FairDrop: Biased Edge Dropout for Enhancing Fairness in Graph Representation Learning, IEEE Transactions on Artificial Intelligence 2021.

Fair edge dropout

We introduce a flexible biased edge dropout algorithm for enhancing fairness in graph representation learning. FairDrop targets the negative effect of the network's homophily w.r.t the sensitive attribute.

Schematics of the proposed framework.

Acknowledgments

Many thanks to the authors of [1] for making their code public and to the maintainers [3] for such an awesome open-source library.

Cite

Please cite our paper if you use this code in your own work:

@ARTICLE{spinelli2021fairdrop,
  author={Spinelli, Indro and Scardapane, Simone and Hussain, Amir and Uncini, Aurelio},
  journal={IEEE Transactions on Artificial Intelligence}, 
  title={FairDrop: Biased Edge Dropout for Enhancing Fairness in Graph Representation Learning}, 
  year={2021},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/TAI.2021.3133818}}

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