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In general, to draw robust conclusions from a dataset, all the analyzedpopulation must be represented on said dataset. Having a dataset that does notfulfill this condition normally leads to selection bias. Additionally, graphshave been used to model a wide variety of problems. Although synthetic graphscan be used to augment available real graph datasets to overcome selectionbias, the generation of unbiased synthetic datasets is complex with currenttools. In this work, we propose a method to find a synthetic graph dataset thathas an even representation of graphs with different metrics. The resultingdataset can then be used, among others, for benchmarking graph processingtechniques as the accuracy of different Graph Neural Network (GNN) models orthe speedups obtained by different graph processing acceleration frameworks.

Bias Reduction via Cooperative Bargaining in Synthetic Graph Dataset Generation