  Recently there has been increasing interest in developing and deploying deep
graph learning algorithms for many tasks, such as fraud detection and
recommender systems. Albeit, there is a limited number of publicly available
graph-structured datasets, most of which are tiny compared to production-sized
applications or are limited in their application domain. This work tackles this
shortcoming by proposing a scalable synthetic graph generation tool to scale
the datasets to production-size graphs with trillions of edges and billions of
nodes. The tool learns a series of parametric models from proprietary datasets
that can be released to researchers to study various graph methods on the
synthetic data increasing prototype development and novel applications. We
demonstrate the generalizability of the framework across a series of datasets,
mimicking structural and feature distributions as well as the ability to scale
them across varying sizes demonstrating their usefulness for benchmarking and
model development. Code can be found on
https://github.com/NVIDIA/DeepLearningExamples/tree/master/Tools/DGLPyTorch/SyntheticGraphGeneration.
