Addressing Shortcomings in Fair Graph Learning Datasets: Towards a New Benchmark

Document Type

Conference Proceeding

Publication Date

8-24-2024

Abstract

Fair graph learning plays a pivotal role in numerous practical applications. Recently, many fair graph learning methods have been proposed; however, their evaluation often relies on poorly constructed semi-synthetic datasets or substandard real-world datasets. In such cases, even a basic Multilayer Perceptron (MLP) can outperform Graph Neural Networks (GNNs) in both utility and fairness. In this work, we illustrate that many datasets fail to provide meaningful information in the edges, which may challenge the necessity of using graph structures in these problems. To address these issues, we develop and introduce a collection of synthetic, semi-synthetic, and real-world datasets that fulfill a broad spectrum of requirements. These datasets are thoughtfully designed to include relevant graph structures and bias information crucial for the fair evaluation of models. The proposed synthetic and semi-synthetic datasets offer the flexibility to create data with controllable bias parameters, thereby enabling the generation of desired datasets with user-defined bias values with ease. Moreover, we conduct systematic evaluations of these proposed datasets and establish a unified evaluation approach for fair graph learning models. Our extensive experimental results with fair graph learning methods across our datasets demonstrate their effectiveness in benchmarking the performance of these methods. Our datasets and the code for reproducing our experiments are available at https://github.com/XweiQ/Benchmark-GraphFairness.

Identifier

85203701079 (Scopus)

ISBN

[9798400704901]

Publication Title

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

External Full Text Location

https://doi.org/10.1145/3637528.3671616

ISSN

2154817X

First Page

5602

Last Page

5612

Grant

2406648

Fund Ref

U.S. Department of Homeland Security

This document is currently not available here.

Share

COinS