PyG 2.0提升图神经网络在真实场景下的可扩展性，支持异构与时序图。
PyG 2.0: Scalable Learning on Real World Graphs
- 重构架构支持异构图和时序图，优化特征与图存储。
- 实现大规模图学习的高效处理，适用于实际应用需求。
- 适合研究者与工程师在复杂图数据上快速构建模型。
自发布以来，PyG（PyTorch Geometric）已发展成为图神经网络领域的领先框架。本文介绍 PyG 2.0 及其后续小版本，带来全面升级，显著提升可扩展性与真实世界应用能力。新版本详细阐述了改进后的框架架构，包括对异构图和时序图的支持、可扩展的特征/图存储机制，以及多种优化策略，使研究人员和实践者能更高效地解决大规模图学习问题。近年来，PyG 已广泛支持各类应用领域，本文将进行总结，并深入探讨关系深度学习和大语言建模等关键方向。
原文摘要 · Abstract (English)
PyG (PyTorch Geometric) has evolved significantly since its initial release, establishing itself as a leading framework for Graph Neural Networks. In this paper, we present Pyg 2.0 (and its subsequent minor versions), a comprehensive update that introduces substantial improvements in scalability and real-world application capabilities. We detail the framework's enhanced architecture, including support for heterogeneous and temporal graphs, scalable feature/graph stores, and various optimizations, enabling researchers and practitioners to tackle large-scale graph learning problems efficiently. Over the recent years, PyG has been supporting graph learning in a large variety of application areas, which we will summarize, while providing a deep dive into the important areas of relational deep learning and large language modeling.
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