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GraphGT: Machine Learning Datasets for Graph Generation and Transformation
Yuanqi Du · Shiyu Wang · Xiaojie Guo · Hengning Cao · Shujie Hu · Junji Jiang · Aishwarya Varala · Abhinav Angirekula · Liang Zhao

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in Dataset and Benchmark Poster Session 4 »
Event URL: https://openreview.net/forum?id=NYgt9vcdyjm »

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{https://graphgt.github.io/} and can also be accessed via an open Python library.

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Author Information

Yuanqi Du (George Mason University)
Shiyu Wang (Emory University)
Shiyu Wang

Shiyu is currently a PhD candidate in Biostatistics at the Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University. He is honored to be advised by Dr. Liang Zhao and Dr. Zhaohui (Steve) Qin, working on deep generative models and graph neural networks.

Xiaojie Guo (JD.COM Silicon Valley Research Center)
Hengning Cao (George Mason University)
Shujie Hu (Emory University)
Junji Jiang (Tianjin University)
Aishwarya Varala (George Mason University)
Abhinav Angirekula (Thomas Jefferson High School for Science and Technology)
Liang Zhao (George Mason University)

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