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）近年来引起了图学习领域的极大关注，已被广泛应用于社交网络、生物图谱等不同领域的实际场景中。图深度学习的研究与应用面临新挑战，包括图数据的稀疏特性、GNN复杂的训练过程以及图任务评估的非标准化。针对这些问题，我们提出了CogDL——一个面向图深度学习的综合库，使研究人员和从业者能够轻松高效地进行实验、比较方法并构建应用。CogDL针对多种图任务的GNN模型训练与评估提出了统一设计方案，这使其在现有图学习库中独具特色。通过采用统一训练器，CogDL能够利用混合精度训练等多种技术优化GNN训练流程。此外，我们为CogDL开发了高效的稀疏算子，使其成为效率最具竞争力的图学习库。CogDL的另一重要特性是注重易用性，旨在促进图学习的开放性和可复现研究。我们利用CogDL报告并维护基础图任务的基准结果，这些结果可被社区复现并直接使用。