{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/graphgt-machine-learning-datasets-for-graph","title":"GraphGT: Machine Learning Datasets for Graph Generation and Transformation","arxiv_id":null,"date":"2021-09-24","proceeding":"NeurIPS Workshop AI4Scien 2021 12","authors":["Yuanqi Du","Shiyu Wang","Xiaojie Guo","Hengning Cao","Shujie Hu","Junji Jiang","Aishwarya Varala","Abhinav Angirekula","Liang Zhao"],"abstract":"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.","url_abs":"https://openreview.net/forum?id=nUktmJLz0up","url_pdf":"https://openreview.net/pdf?id=nUktmJLz0up","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"graphgt-machine-learning-datasets-for-graph","repo_url":"https://github.com/yuanqidu/graphgt","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"drug-design","task_name":"Drug Design"},{"task_slug":"graph-generation","task_name":"Graph Generation"},{"task_slug":"graph-representation-learning","task_name":"Graph Representation Learning"},{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}