2022/05/27 by Axel Wassington, Wassington, Axel, Sergi Abadal +1
Computer Science · #Advanced Graph Neural Networks #Cloud Computing and Resource Management #FOS: Computer and information sciences #Graph Theory and Algorithms #Machine Learning (cs.LG)
paper · pdf · doi:10.48550/arxiv.2205.13901
openalex publication_date 2022/05/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.