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

We investigate the concept of network momentum, a novel trading signal
derived from momentum spillover across assets. Initially observed within the
confines of pairwise economic and fundamental ties, such as the stock-bond
connection of the same company and stocks linked through supply-demand chains,
momentum spillover implies a propagation of momentum risk premium from one
asset to another. The similarity of momentum risk premium, exemplified by
co-movement patterns, has been spotted across multiple asset classes including
commodities, equities, bonds and currencies. However, studying the network
effect of momentum spillover across these classes has been challenging due to a
lack of readily available common characteristics or economic ties beyond the
company level. In this paper, we explore the interconnections of momentum
features across a diverse range of 64 continuous future contracts spanning
these four classes. We utilise a linear and interpretable graph learning model
with minimal assumptions to reveal the intricacies of the momentum spillover
network. By leveraging the learned networks, we construct a network momentum
strategy that exhibits a Sharpe ratio of 1.5 and an annual return of 22%, after
volatility scaling, from 2000 to 2022. This paper pioneers the examination of
momentum spillover across multiple asset classes using only pricing data,
presents a multi-asset investment strategy based on network momentum, and
underscores the effectiveness of this strategy through robust empirical
analysis.

本研究通过仅使用定价数据，探讨了跨多个资产类别的动量溢出，提出了基于网络动量的多资产投资策略，并通过稳健的经验分析，强调该策略的有效性。