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

arXiv:2403.06017v1 Announce Type: new
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 …

arxiv benchmark cs.cy cs.lg datasets fair graph graph learning type