论文标题
通过合成图数据集生成合作讨价还价的偏差减少
Bias Reduction via Cooperative Bargaining in Synthetic Graph Dataset Generation
论文作者
论文摘要
通常，要从数据集中得出强大的结论，必须在上述数据集中表示所有分析的人群。拥有无法满足此条件的数据集通常会导致选择偏差。此外，图表已被用于建模各种问题。尽管合成图可用于增强可用的真实图形数据集来克服选择偏差，但无偏的合成数据集的生成与当前工具很复杂。在这项工作中，我们提出了一种方法，可以找到一个具有不同指标的图形表示的合成图数据集。然后，可以将结果数据集用于基准图形处理技术作为不同图形神经网络（GNN）模型的准确性或通过不同的图形处理加速框架获得的加速度的准确性。
In general, to draw robust conclusions from a dataset, all the analyzed population must be represented on said dataset. Having a dataset that does not fulfill this condition normally leads to selection bias. Additionally, graphs have been used to model a wide variety of problems. Although synthetic graphs can be used to augment available real graph datasets to overcome selection bias, the generation of unbiased synthetic datasets is complex with current tools. In this work, we propose a method to find a synthetic graph dataset that has an even representation of graphs with different metrics. The resulting dataset can then be used, among others, for benchmarking graph processing techniques as the accuracy of different Graph Neural Network (GNN) models or the speedups obtained by different graph processing acceleration frameworks.
