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Data"],"published-print":{"date-parts":[[2025,9,22]]},"abstract":"<jats:p>\n            Temporal graph neural networks (T-GNNs) are crucial for modeling dynamic graphs, capturing evolving structures and interactions to address complex temporal properties in applications like event prediction, dynamic social network analysis, and temporal knowledge graph reasoning. However, training T-GNNs is hampered by the massive scale of graphs and complex temporal dynamics, leading to significant runtime and memory efficiency challenges. To tackle these challenges, this paper proposes\n            <jats:italic toggle=\"yes\">SWIFT,<\/jats:italic>\n            the first secondary memory-based T-GNN training system for large-scale temporal graph learning on a single machine. SWIFT employs a novel bucket-based pipeline parallelism strategy to efficiently manage data flows across GPU, main, and secondary memories, addressing the computation and memory bottlenecks that hinder scaling for large-scale temporal graphs. Remarkably, SWIFT surpasses its main memory-based counterparts in runtime efficiency while requiring significantly less main memory. Extensive experiments demonstrate that SWIFT achieves up to a 4.3\u00d7 speedup and a 7.9X reduction in main memory usage compared to state-of-the-art baselines on large temporal graphs.\n          <\/jats:p>","DOI":"10.1145\/3749184","type":"journal-article","created":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T17:17:03Z","timestamp":1758647823000},"page":"1-27","source":"Crossref","is-referenced-by-count":0,"title":["SWIFT: Enabling Large-Scale Temporal Graph Learning on a Single Machine"],"prefix":"10.1145","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-4033-3816","authenticated-orcid":false,"given":"Rui","family":"Guo","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence and Data Science, USTC, Hefei, Anhui, China and Data Darkness Lab, Suzhou Institute for Advanced Research, USTC, Suzhou, Jiangsu, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6286-8679","authenticated-orcid":false,"given":"Zezhong","family":"Ding","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence and Data Science, USTC, Hefei, Anhui, China and Data Darkness Lab, Suzhou Institute for Advanced Research, USTC, Suzhou, Jiangsu, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5290-5408","authenticated-orcid":false,"given":"Xike","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, USTC, Suzhou, Jiangsu, China and Data Darkness Lab, Suzhou Institute for Advanced Research, USTC, Suzhou, Jiangsu, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9404-5848","authenticated-orcid":false,"given":"Jianliang","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Hong Kong Baptist University, Hong Kong SAR, China"}]}],"member":"320","published-online":{"date-parts":[[2025,9,23]]},"reference":[{"key":"e_1_2_1_1_1","first-page":"1190","article-title":"CoLES","author":"Babaev Dmitrii","year":"2022","unstructured":"Dmitrii Babaev, Nikita Ovsov, Ivan Kireev, Mariya Ivanova, Gleb Gusev, Ivan Nazarov, and Alexander Tuzhilin. 2022. 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