FairCAT-generated datasets for benchmarking fairness-aware GNNs

These synthetic datasets are created with an aim to be used for benchmarking fairness and performance of Graph Neural Networks (GNNs). The datasets are created specifically for the following experiments: balancing, correlations strength, data scaling, and synthetic German Credit, and synthetic Pokec_n. The environment for each experiment varies in only one variable, according to the experiment, while others stay fixed. Balancing experiment: the ratio of the sensitive groups is varied for each graph: Balanced (50/50 split), Mild Imbalance (70/30), and Strong Imbalance (90/10). Correlation strength experiment: graphs with varying strength of correlation between sensitive attribute and a non-sensitive attribute: Low (0.05), Medium (0.50), and High (0.95). Scaling experiment: graphs are increasing in size: Small (number of nodes=2^15), Medium (nodes=2^20), Large (nodes=2^23; called 2^25 due to an error). German FairCAT: a synthetic dataset that tries to reproduce real-world German Credit dataset. It can be used to compare GNN predictions with learning on the original dataset. Pokec_n FairCAT: a synthetic dataset that tries to reproduce real-world Pokec_n dataset.本批合成数据集旨在用于图神经网络（Graph Neural Networks，GNNs）的公平性与性能基准测试。本数据集专为以下几类实验构建：公平性均衡、关联强度、数据缩放，以及合成德国信贷（synthetic German Credit）与合成Pokec_n数据集。每类实验仅对单一变量进行调整，其余变量均保持固定。 公平性均衡实验：针对每张图调整敏感组的占比，设置三种场景：均衡（50/50划分）、轻度失衡（70/30划分）与重度失衡（90/10划分）。 关联强度实验：设置敏感属性与非敏感属性间关联强度各异的图数据集，分为低关联（0.05）、中关联（0.50）与高关联（0.95）三个档位。 数据缩放实验：使用节点规模递增的图数据集，分为小样本（节点数=2^15）、中样本（节点数=2^20）与大样本（节点数=2^23；因笔误标注为2^25）。 German FairCAT：旨在复刻真实世界德国信贷数据集的合成数据集，可用于对比GNN模型在该合成数据集与原始数据集上的预测性能。 Pokec_n FairCAT：旨在复刻真实世界Pokec_n数据集的合成数据集。