Graph neural networks (GNNs) have attracted tremendous attention from the graph learning community in recent years. It has been widely adopted in various real-world applications from diverse domains, such as social networks and biological graphs. The research and applications of graph deep learning present new challenges, including the sparse nature of graph data, complicated training of GNNs, and non-standard evaluation of graph tasks. To tackle the issues, we present CogDL, a comprehensive library for graph deep learning that allows researchers and practitioners to conduct experiments, compare methods, and build applications with ease and efficiency. In CogDL, we propose a unified design for the training and evaluation of GNN models for various graph tasks, making it unique among existing graph learning libraries. By utilizing this unified trainer, CogDL can optimize the GNN training loop with several training techniques, such as mixed precision training. Moreover, we develop efficient sparse operators for CogDL, enabling it to become the most competitive graph library for efficiency. Another important CogDL feature is its focus on ease of use with the aim of facilitating open and reproducible research of graph learning. We leverage CogDL to report and maintain benchmark results on fundamental graph tasks, which can be reproduced and directly used by the community.
翻译:图神经网络(GNN)近年来吸引了图学习社区的极大关注。它已被广泛采用于来自不同领域(如社交网络和生物图像)的各种实际应用中。图深度学习的研究和应用提出了新的挑战, 包括图形数据的稀疏性、GNN的复杂训练和图任务的非标准评估等。为了解决这些问题,我们提出了CogDL,这是一个用于图深度学习的综合性库,可以让研究人员和实践者轻松高效地进行实验、比较方法和构建应用。在CogDL中,我们为训练和评估 GNN 模型提出了统一的设计。这是现有图学习库中独一无二的。通过利用这个统一的训练器,CogDL可以使用几种训练技术(如混合精度训练)优化 GNN 训练循环。此外,我们为CogDL开发了高效的稀疏算子,使其成为效率最高的图库之一。CogDL的另一个重要特点是它注重易用性,旨在促进图学习的开放和可再现性研究。我们利用CogDL报告并维护了基本图任务的基准结果,这些结果可以被社区复制,并直接使用。