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FairDrop: Biased Edge Dropout for Enhancing Fairness in Graph Representation Learning

Spinelli, Indro; Scardapane, Simone; Hussain, Amir; Uncini, Aurelio

Authors

Indro Spinelli

Simone Scardapane

Aurelio Uncini



Abstract

Graph representation learning has become a ubiquitous component in many scenarios, ranging from social network analysis to energy forecasting in smart grids. In several applications, ensuring the fairness of the node (or graph) representations with respect to some protected attributes is crucial for their correct deployment. Yet, fairness in graph deep learning remains underexplored, with few solutions available. In particular, the tendency of similar nodes to cluster on several real-world graphs (i.e., homophily) can dramatically worsen the fairness of these procedures. In this article, we propose a novel biased edge dropout algorithm (FairDrop) to counter-act homophily and improve fairness in graph representation learning. FairDrop can be plugged in easily on many existing algorithms, is efficient, adaptable, and can be combined with other fairness-inducing solutions. After describing the general algorithm, we demonstrate its application on two benchmark tasks, specifically, as a ran...

Journal Article Type Article
Online Publication Date Dec 10, 2021
Publication Date 2022-06
Deposit Date Nov 21, 2022
Journal IEEE Transactions on Artificial Intelligence
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 3
Issue 3
Pages 344-354
DOI https://doi.org/10.1109/tai.2021.3133818
Keywords Fairness, graph embedding, graph neural network, graph representation learning, link prediction
Public URL http://researchrepository.napier.ac.uk/Output/2963037