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arXiv · 2507.16991

PyG 2.0: Scalable Learning on Real World Graphs

Matthias Fey·Jinu Sunil·Akihiro Nitta·Rishi Puri·Manan Shah·Blaž Stojanovič·Ramona Bendias·Alexandria Barghi·Vid Kocijan·Zecheng Zhang·Xinwei He·Jan Eric Lenssen·Jure Leskovec
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Abstract

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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Matthias Fey, Jinu Sunil, Akihiro Nitta, Rishi Puri, Manan Shah, Blaž Stojanovič, Ramona Bendias, Alexandria Barghi, Vid Kocijan, Zecheng Zhang, Xinwei He, Jan Eric Lenssen, Jure Leskovec. 2025-07-22. PyG 2.0: Scalable Learning on Real World Graphs. https://arxiv.org/abs/2507.16991

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