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

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...

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Bibliographic Details
Main Authors:	Hussain, A. (Author), Scardapane, S. (Author), Spinelli, I. (Author), Uncini, A. (Author)
Format:	Article
Language:	English
Published:	Institute of Electrical and Electronics Engineers Inc. 2022
Subjects:	Benchmarking Convolutional neural networks Deep learning Embeddings Energy forecasting Fairness Forecasting Graph embedding Graph embeddings Graph neural network Graph neural networks Graph representation Graph representation learning Graph structures Graph theory Homophily Link prediction Social Network Analysis
Online Access:	View Fulltext in Publisher

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

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