Graph Learning Indexer：一个贡献者友好且元数据丰富的图学习基准平台
Graph Learning Indexer: A Contributor-Friendly and Metadata-Rich Platform for Graph Learning Benchmarks
摘要：
建立开放和通用的基准一直是现代机器学习技术成功背后的关键驱动力。随着机器学习被应用于更广泛的领域和任务，需要建立更丰富、更多样化的基准，以更好地反映应用场景的现实。图学习是机器学习的一个新兴领域，迫切需要更多更好的基准。为了适应这种需求，我们引入了图学习索引器（GLI），这是一个用于图学习的基准策展平台。与现有的图学习基准库相比，GLI突出了两个新的设计目标。首先，全球领导能力倡议旨在激励\emph{dataset contributors}。特别是，我们采用了各种措施来最大限度地减少贡献和维护数据集的工作，提高贡献数据集的可用性，并鼓励将属性归因于数据集的不同贡献者。其次，GLI旨在管理基准数据集的知识库，而不是普通的集合。我们使用多个Meta信息源来用\emph{rich characteristics}增强基准数据集，以便它们可以很容易地被选择并用于下游研究或开发。GLI的源代码可在\url{https://github.com/Graph-Learning-Benchmarks/gli}上获得。
英文摘要：
Establishing open and general benchmarks has been a critical driving force behind the success of modern machine learning techniques. As machine learning is being applied to broader domains and tasks, there is a need to establish richer and more diverse benchmarks to better reflect the reality of the application scenarios. Graph learning is an emerging field of machine learning that urgently needs more and better benchmarks. To accommodate the need, we introduce Graph Learning Indexer (GLI), a benchmark curation platform for graph learning. In comparison to existing graph learning benchmark libraries, GLI highlights two novel design objectives. First, GLI is designed to incentivize \emph{dataset contributors}. In particular, we incorporate various measures to minimize the effort of contributing and maintaining a dataset, increase the usability of the contributed dataset, as well as encourage attributions to different contributors of the dataset. Second, GLI is designed to curate a knowledge base, instead of a plain collection, of benchmark datasets. We use multiple sources of meta information to augment the benchmark datasets with \emph{rich characteristics}, so that they can be easily selected and used in downstream research or development. The source code of GLI is available at \url{https://github.com/Graph-Learning-Benchmarks/gli}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
