{"ID":2891962,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.16991","arxiv_id":"2507.16991","title":"PyG 2.0: Scalable Learning on Real World Graphs","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.","short_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 applicat...","url_abs":"https://arxiv.org/abs/2507.16991","url_pdf":"https://arxiv.org/pdf/2507.16991v2","authors":"[\"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\"]","published":"2025-07-22T19:55:09Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\"]","methods":"[\"Graph Neural Network\",\"Language Model\"]","has_code":false}