GraphGT: Machine Learning Datasets for Graph Generation and Transformation

Part of Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1 (NeurIPS Datasets and Benchmarks 2021) round2

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Yuanqi Du, Shiyu Wang, Xiaojie Guo, Hengning Cao, Shujie Hu, Junji Jiang, Aishwarya Varala, Abhinav Angirekula, Liang Zhao


Graph generation has shown great potential in applications like network design and mobility synthesis and is one of the fastest-growing domains in machine learning for graphs. Despite the success of graph generation, the corresponding real-world datasets are few and limited to areas such as molecules and citation networks. To fill the gap, we introduce GraphGT, a large dataset collection for graph generation and transformation problem, which contains 36 datasets from 9 domains across 6 subjects. To assist the researchers with better explorations of the datasets, we provide a systemic review and classification of the datasets based on research tasks, graph types, and application domains. We have significantly (re)processed all the data from different domains to fit the unified framework of graph generation and transformation problems. In addition, GraphGT provides an easy-to-use graph generation pipeline that simplifies the process for graph data loading, experimental setup and model evaluation. Finally, we compare the performance of popular graph generative models in 16 graph generation and 17 graph transformation datasets, showing the great power of GraphGT in differentiating and evaluating model capabilities and drawbacks. GraphGT has been regularly updated and welcomes inputs from the community. GraphGT is publicly available at \url{} and can also be accessed via an open Python library.