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Synthetic Graph Generation to Benchmark Graph Learning
[article]
2022
arXiv
pre-print
Graph learning algorithms have attained state-of-the-art performance on many graph analysis tasks such as node classification, link prediction, and clustering. It has, however, become hard to track the field's burgeoning progress. One reason is due to the very small number of datasets used in practice to benchmark the performance of graph learning algorithms. This shockingly small sample size (~10) allows for only limited scientific insight into the problem. In this work, we aim to address this
arXiv:2204.01376v1
fatcat:fwjn5yrqkveljfgq6ep3tjkebm