Recently, there has been increasing interest in developing and deploying deep graph learning algorithms for various tasks, such as fraud detection and recommender systems. However, there is a limited number of publicly available graph-structured datasets, most of which are small compared with production-sized applications or limited in their application domain. In this work, we tackle this shortcoming by proposing a synthetic graph generation tool that enables scaling datasets to production-size graphs with trillions of edges and billions of nodes. The proposed method comprises a series of parametric models that can either be randomly initialized or fit to proprietary datasets. These models can then be released to researchers to study graph methods on the synthetic data, facilitating prototype development and novel applications. We demonstrate the generalizability of the framework across various datasets, mimicking their structural and feature distributions, as well as the ability to scale them to varying sizes, demonstrating their usefulness for benchmarking and model development. Code can be found on GitHub.

Deep learning for time series is an emerging area with close ties to industry, yet under represented in performance benchmarks for machine learning systems. In this paper, we present a landscape of deep learning applications applied to time series, and discuss the challenges and directions towards building a robust performance benchmark of deep learning workloads for time series data.