  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.
