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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 AI for Science: Mind the Gaps »
Event URL: https://openreview.net/forum?id=nUktmJLz0up »

Graph generation, which learns from known graphs and discovers novel graphs, has great potential in numerous research topics like drug design and mobility synthesis and is one of the fastest-growing domains recently due to its promise for discovering new knowledge. Though many benchmark datasets have emerged in the domain of graph representation learning, the real-world datasets for graph generation problem are much fewer and limited to a small number of areas such as molecules and citation networks. To fill the gap, we introduce GraphGT, a large dataset collection for graph generation problem in machine learning, 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 from various views including research tasks, graph types, and application domains. In addition, GraphGT provides an easy-to-use graph generation pipeline that simplifies the process for graph data loading, experimental setup, model evaluation. The community can query and access datasets of interest according to a specific domain, task, or type of graph. GraphGT will be regularly updated and welcome inputs from the community. GraphGT is publicly available at \url{https://graphgt.github.io/} and can also be accessed via an open Python library.

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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