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
this https URL

公平图学习方法的评估常常依赖于构造不完善的半合成数据集或次标准的真实世界数据集。本研究针对这些问题，通过开发和引入一系列合成、半合成和真实世界数据集，旨在为公平模型的评估提供有意义的图结构和关键偏见信息。经过对提出的数据集的系统评估，为公平图学习模型建立了一种统一的评估方法。我们在数据集上对公平图学习方法进行了广泛的实验，证明了这些方法在性能评估方面的有效性。

解决公平图学习数据集的不足：走向新的基准

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

We study deceptive fairness attacks on graphs to answer the following
question: How can we achieve poisoning attacks on a graph learning model to
exacerbate the bias deceptively? We answer this question via a bi-level
optimization problem and propose a meta learning-based framework named FATE.
FATE is broadly applicable with respect to various fairness definitions and
graph learning models, as well as arbitrary choices of manipulation operations.
We further instantiate FATE to attack statistical parity and individual
fairness on graph neural networks. We conduct extensive experimental
evaluations on real-world datasets in the task of semi-supervised node
classification. The experimental results demonstrate that FATE could amplify
the bias of graph neural networks with or without fairness consideration while
maintaining the utility on the downstream task. We hope this paper provides
insights into the adversarial robustness of fair graph learning and can shed
light on designing robust and fair graph learning in future studies.

我们研究了图上的欺骗性公平攻击以回答以下问题：我们如何通过毒化图学习模型来欺骗性地加剧偏见？我们通过双层优化问题回答了这个问题，并提出了一个名为 FATE 的元学习框架。FATE 可广泛适用于各种公平定义和图学习模型，以及任意选择的操作方法。我们进一步实例化 FATE，对图神经网络上的统计平等和个体公平进行攻击。我们在半监督节点分类任务的真实数据集上进行了广泛的实验评估。实验结果表明，FATE 可以在考虑或不考虑公平性的情况下放大图神经网络的偏见，同时保持下游任务的效用。我们希望本文能为公平图学习的对抗鲁棒性提供启示，并为未来的研究设计稳健和公平的图学习提供指导。

通过元学习对图数据进行欺诈性公平性攻击

Deceptive Fairness Attacks on Graphs via Meta Learning

Tackling unfairness in graph learning models is a challenging task, as the
unfairness issues on graphs involve both attributes and topological structures.
Existing work on fair graph learning simply assumes that attributes of all
nodes are available for model training and then makes fair predictions. In
practice, however, the attributes of some nodes might not be accessible due to
missing data or privacy concerns, which makes fair graph learning even more
challenging. In this paper, we propose FairAC, a fair attribute completion
method, to complement missing information and learn fair node embeddings for
graphs with missing attributes. FairAC adopts an attention mechanism to deal
with the attribute missing problem and meanwhile, it mitigates two types of
unfairness, i.e., feature unfairness from attributes and topological unfairness
due to attribute completion. FairAC can work on various types of homogeneous
graphs and generate fair embeddings for them and thus can be applied to most
downstream tasks to improve their fairness performance. To our best knowledge,
FairAC is the first method that jointly addresses the graph attribution
completion and graph unfairness problems. Experimental results on benchmark
datasets show that our method achieves better fairness performance with less
sacrifice in accuracy, compared with the state-of-the-art methods of fair graph
learning. Code is available at: this https URL.

本文提出了一种名为 FairAC 的公平属性补全方法，采用注意力机制来处理属性丢失问题，并同时减少因属性完整性而导致的两种不公平性，通过在基准数据集上进行实验，结果表明我们的方法在公平性性能方面比公平图学习的现有方法表现更好且牺牲较少的准确性。