Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
2-2023
Abstract
Conventional graph neural networks (GNNs) are often confronted with fairness issues that may stem from their input, including node attributes and neighbors surrounding a node. While several recent approaches have been proposed to eliminate the bias rooted in sensitive attributes, they ignore the other key input of GNNs, namely the neighbors of a node, which can introduce bias since GNNs hinge on neighborhood structures to generate node representations. In particular, the varying neighborhood structures across nodes, manifesting themselves in drastically different node degrees, give rise to the diverse behaviors of nodes and biased outcomes. In this paper, we first define and generalize the degree bias using a generalized definition of node degree as a manifestation and quantification of different multi-hop structures around different nodes. To address the bias in the context of node classification, we propose a novel GNN framework called Generalized Degree Fairness-centric Graph Neural Network (DegFairGNN). Specifically, in each GNN layer, we employ a learnable debiasing function to generate debiasing contexts, which modulate the layer-wise neighborhood aggregation to eliminate the degree bias originating from the diverse degrees among nodes. Extensive experiments on three benchmark datasets demonstrate the effectiveness of our model on both accuracy and fairness metrics.
Keywords
De-biasing, Graph neural networks, Layer-wise, Multi-hops, Neighborhood structure, Network frameworks, Node attribute, Node degree, Sensitive attribute
Discipline
Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
Proceedings of the 37th AAAI Conference on Artificial Intelligence, Washington, USA, 2023 February 7-14
First Page
4525
Last Page
4533
Identifier
10.48550/arXiv.2302.03881
Publisher
AAAI Press
City or Country
Washington DC, USA
Citation
LIU, Zemin; NGUYEN, Trung Kien; and FANG, Yuan.
On generalized degree fairness in graph neural networks. (2023). Proceedings of the 37th AAAI Conference on Artificial Intelligence, Washington, USA, 2023 February 7-14. 4525-4533.
Available at: https://ink.library.smu.edu.sg/sis_research/8189
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://doi.org/10.48550/arXiv.2302.03881