A Framework for Large Scale Synthetic Graph Dataset Generation. (arXiv:2210.01944v2 [cs.LG] UPDATED)

Recently there has been increasing interest in developing and deploying deep
graph learning algorithms for many graph analysis tasks such as node and edge
classification, link prediction, and clustering with numerous practical
applications such as fraud detection, drug discovery, or recommender systems.
Allbeit there is a limited number of publicly available graph-structured
datasets, most of which are tiny compared to production-sized applications with
trillions of edges and billions of nodes. Further, new algorithms and models
are benchmarked across similar datasets …

arxiv dataset dataset generation framework graph scale