Bias Reduction via Cooperative Bargaining in Synthetic Graph Dataset Generation
This addresses bias reduction for researchers and practitioners in graph-based machine learning, though it appears incremental as it builds on existing synthetic graph generation tools.
The paper tackles the problem of selection bias in graph datasets by proposing a method to generate synthetic graph datasets with even representation of graphs across different metrics, enabling their use for benchmarking graph processing techniques like GNN models and acceleration frameworks.
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.