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arxiv: 2507.16991 · v2 · pith:UIQCBVW6new · submitted 2025-07-22 · 💻 cs.LG · cs.AI

PyG 2.0: Scalable Learning on Real World Graphs

Matthias Fey , Jinu Sunil , Akihiro Nitta , Rishi Puri , Manan Shah , Bla\v{z} Stojanovi\v{c} , Ramona Bendias , Alexandria Barghi
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Vid Kocijan Zecheng Zhang Xinwei He Jan Eric Lenssen Jure Leskovec
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classification 💻 cs.LG cs.AI
keywords graphlearningapplicationareasdeepframeworkgraphslarge
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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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