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Recently there has been increasing interest in developing and deploying deepgraph learning algorithms for many tasks, such as fraud detection andrecommender systems. Albeit, there is a limited number of publicly availablegraph-structured datasets, most of which are tiny compared to production-sizedapplications or are limited in their application domain. This work tackles thisshortcoming by proposing a scalable synthetic graph generation tool to scalethe datasets to production-size graphs with trillions of edges and billions ofnodes. The tool learns a series of parametric models from proprietary datasetsthat can be released to researchers to study various graph methods on thesynthetic data increasing prototype development and novel applications. Wedemonstrate the generalizability of the framework across a series of datasets,mimicking structural and feature distributions as well as the ability to scalethem across varying sizes demonstrating their usefulness for benchmarking andmodel development. Code can be found onhttps://github.com/NVIDIA/DeepLearningExamples/tree/master/Tools/DGLPyTorch/SyntheticGraphGeneration.

A Framework for Large Scale Synthetic Graph Dataset Generation