{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T10:03:52Z","timestamp":1775815432766,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":30,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,11,11]],"date-time":"2023-11-11T00:00:00Z","timestamp":1699660800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["OAC-2209563"],"award-info":[{"award-number":["OAC-2209563"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,11,12]]},"DOI":"10.1145\/3581784.3607056","type":"proceedings-article","created":{"date-parts":[[2023,11,14]],"date-time":"2023-11-14T21:47:06Z","timestamp":1699998426000},"page":"1-12","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":18,"title":["DistTGL: Distributed Memory-Based Temporal Graph Neural Network Training"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0001-8158-3081","authenticated-orcid":false,"given":"Hongkuan","family":"Zhou","sequence":"first","affiliation":[{"name":"University of Southern California, Los Angeles, United States of America"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0001-8115-5415","authenticated-orcid":false,"given":"Da","family":"Zheng","sequence":"additional","affiliation":[{"name":"AWS AI, Santa Clara, United States of America"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0001-5030-5054","authenticated-orcid":false,"given":"Xiang","family":"Song","sequence":"additional","affiliation":[{"name":"AWS AI, Santa Clara, United States of America"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0003-2753-1437","authenticated-orcid":false,"given":"George","family":"Karypis","sequence":"additional","affiliation":[{"name":"AWS AI, Santa Clara, United States of America"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-1609-8589","authenticated-orcid":false,"given":"Viktor","family":"Prasanna","sequence":"additional","affiliation":[{"name":"University of Southern California, Los Angeles, United States of America"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,11,11]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Alibaba. 2020. Euler-2.0. https:\/\/2.zoppoz.workers.dev:443\/https\/github.com\/alibaba\/euler.  Alibaba. 2020. Euler-2.0. https:\/\/2.zoppoz.workers.dev:443\/https\/github.com\/alibaba\/euler."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447786.3456233"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3458817.3480858"},{"key":"e_1_3_2_1_4_1","unstructured":"Xinshi Chen Yan Zhu Haowen Xu Mengyang Liu Liang Xiong Muhan Zhang and Le Song. 2021. Efficient Dynamic Graph Representation Learning at Scale. arXiv:2112.07768 [cs.LG]  Xinshi Chen Yan Zhu Haowen Xu Mengyang Liu Liang Xiong Muhan Zhang and Le Song. 2021. Efficient Dynamic Graph Representation Learning at Scale. arXiv:2112.07768 [cs.LG]"},{"key":"e_1_3_2_1_5_1","volume-title":"Fast Graph Representation Learning with PyTorch Geometric. In ICLR Workshop on Representation Learning on Graphs and Manifolds.","author":"Fey Matthias","unstructured":"Matthias Fey and Jan E. Lenssen . 2019 . Fast Graph Representation Learning with PyTorch Geometric. In ICLR Workshop on Representation Learning on Graphs and Manifolds. Matthias Fey and Jan E. Lenssen. 2019. Fast Graph Representation Learning with PyTorch Geometric. In ICLR Workshop on Representation Learning on Graphs and Manifolds."},{"key":"e_1_3_2_1_6_1","volume-title":"Ninareh Mehrabi, Emilio Ferrara, and Arquimedes Canedo.","author":"Goyal Palash","year":"2018","unstructured":"Palash Goyal , Sujit Rokka Chhetri , Ninareh Mehrabi, Emilio Ferrara, and Arquimedes Canedo. 2018 . DynamicGEM: A Library for Dynamic Graph Embedding Methods . arXiv preprint arXiv:1811.10734 (2018). Palash Goyal, Sujit Rokka Chhetri, Ninareh Mehrabi, Emilio Ferrara, and Arquimedes Canedo. 2018. DynamicGEM: A Library for Dynamic Graph Embedding Methods. arXiv preprint arXiv:1811.10734 (2018)."},{"key":"e_1_3_2_1_7_1","unstructured":"Ehsan Hajiramezanali Arman Hasanzadeh Krishna Narayanan Nick Duffield Mingyuan Zhou and Xiaoning Qian. 2019. Variational graph recurrent neural networks. In Advances in Neural Information Processing Systems. 10700--10710.  Ehsan Hajiramezanali Arman Hasanzadeh Krishna Narayanan Nick Duffield Mingyuan Zhou and Xiaoning Qian. 2019. Variational graph recurrent neural networks. In Advances in Neural Information Processing Systems. 10700--10710."},{"key":"e_1_3_2_1_8_1","volume-title":"METIS: A software package for partitioning unstructured graphs, partitioning meshes, and computing fill-reducing orderings of sparse matrices.","author":"Karypis George","year":"1997","unstructured":"George Karypis and Vipin Kumar . 1997 . METIS: A software package for partitioning unstructured graphs, partitioning meshes, and computing fill-reducing orderings of sparse matrices. (1997). George Karypis and Vipin Kumar. 1997. METIS: A software package for partitioning unstructured graphs, partitioning meshes, and computing fill-reducing orderings of sparse matrices. (1997)."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330895"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330895"},{"key":"e_1_3_2_1_11_1","volume-title":"Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence.","author":"Pareja Aldo","unstructured":"Aldo Pareja , Giacomo Domeniconi , Jie Chen , Tengfei Ma , Toyotaro Suzumura , Hiroki Kanezashi , Tim Kaler , Tao B. Schardl , and Charles E. Leiserson . 2020. EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs . In Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence. Aldo Pareja, Giacomo Domeniconi, Jie Chen, Tengfei Ma, Toyotaro Suzumura, Hiroki Kanezashi, Tim Kaler, Tao B. Schardl, and Charles E. Leiserson. 2020. EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs. In Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence."},{"key":"e_1_3_2_1_12_1","volume-title":"PyTorch: An Imperative Style","author":"Paszke Adam","unstructured":"Adam Paszke , Sam Gross , Francisco Massa , Adam Lerer , James Bradbury , Gregory Chanan , Trevor Killeen , Zeming Lin , Natalia Gimelshein , Luca Antiga , Alban Desmaison , Andreas Kopf , Edward Yang , Zachary DeVito , Martin Raison , Alykhan Tejani , Sasank Chilamkurthy , Benoit Steiner , Lu Fang , Junjie Bai , and Soumith Chintala . 2019. PyTorch: An Imperative Style , High-Performance Deep Learning Library . In Advances in Neural Information Processing Systems 32. Curran Associates, Inc., 8024--8035. Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019. PyTorch: An Imperative Style, High-Performance Deep Learning Library. In Advances in Neural Information Processing Systems 32. Curran Associates, Inc., 8024--8035."},{"key":"e_1_3_2_1_13_1","volume-title":"Towards Better Evaluation for Dynamic Link Prediction. In Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track. https:\/\/2.zoppoz.workers.dev:443\/https\/openreview.net\/forum?id=1GVpwr2Tfdg","author":"Poursafaei Farimah","year":"2022","unstructured":"Farimah Poursafaei , Andy Huang , Kellin Pelrine , and Reihaneh Rabbany . 2022 . Towards Better Evaluation for Dynamic Link Prediction. In Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track. https:\/\/2.zoppoz.workers.dev:443\/https\/openreview.net\/forum?id=1GVpwr2Tfdg Farimah Poursafaei, Andy Huang, Kellin Pelrine, and Reihaneh Rabbany. 2022. Towards Better Evaluation for Dynamic Link Prediction. In Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track. https:\/\/2.zoppoz.workers.dev:443\/https\/openreview.net\/forum?id=1GVpwr2Tfdg"},{"key":"e_1_3_2_1_14_1","volume-title":"Temporal Graph Networks for Deep Learning on Dynamic Graphs. In ICML 2020 Workshop on Graph Representation Learning.","author":"Rossi Emanuele","year":"2020","unstructured":"Emanuele Rossi , Ben Chamberlain , Fabrizio Frasca , Davide Eynard , Federico Monti , and Michael Bronstein . 2020 . Temporal Graph Networks for Deep Learning on Dynamic Graphs. In ICML 2020 Workshop on Graph Representation Learning. Emanuele Rossi, Ben Chamberlain, Fabrizio Frasca, Davide Eynard, Federico Monti, and Michael Bronstein. 2020. Temporal Graph Networks for Deep Learning on Dynamic Graphs. In ICML 2020 Workshop on Graph Representation Learning."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371845"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1057\/s41265-016-0029-z"},{"key":"e_1_3_2_1_17_1","volume-title":"Proceedings of the 34th International Conference on Machine Learning -","volume":"70","author":"Trivedi Rakshit","year":"2017","unstructured":"Rakshit Trivedi , Hanjun Dai , Yichen Wang , and Le Song . 2017 . Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs . In Proceedings of the 34th International Conference on Machine Learning - Volume 70 (Sydney, NSW, Australia) (ICML'17). JMLR.org, 3462--3471. Rakshit Trivedi, Hanjun Dai, Yichen Wang, and Le Song. 2017. Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs. In Proceedings of the 34th International Conference on Machine Learning - Volume 70 (Sydney, NSW, Australia) (ICML'17). JMLR.org, 3462--3471."},{"key":"e_1_3_2_1_18_1","volume-title":"International Conference on Learning Representations. https:\/\/2.zoppoz.workers.dev:443\/https\/openreview.net\/forum?id=HyePrhR5KX","author":"Trivedi Rakshit","year":"2019","unstructured":"Rakshit Trivedi , Mehrdad Farajtabar , Prasenjeet Biswal , and Hongyuan Zha . 2019 . DyRep: Learning Representations over Dynamic Graphs . In International Conference on Learning Representations. https:\/\/2.zoppoz.workers.dev:443\/https\/openreview.net\/forum?id=HyePrhR5KX Rakshit Trivedi, Mehrdad Farajtabar, Prasenjeet Biswal, and Hongyuan Zha. 2019. DyRep: Learning Representations over Dynamic Graphs. In International Conference on Learning Representations. https:\/\/2.zoppoz.workers.dev:443\/https\/openreview.net\/forum?id=HyePrhR5KX"},{"key":"e_1_3_2_1_19_1","volume-title":"Highly-Performant Package for Graph Neural Networks. arXiv preprint arXiv:1909.01315","author":"Wang Minjie","year":"2019","unstructured":"Minjie Wang , Da Zheng , Zihao Ye , Quan Gan , Mufei Li , Xiang Song , Jinjing Zhou , Chao Ma , Lingfan Yu , Yu Gai , Tianjun Xiao , Tong He , George Karypis , Jinyang Li , and Zheng Zhang . 2019. Deep Graph Library: A Graph-Centric , Highly-Performant Package for Graph Neural Networks. arXiv preprint arXiv:1909.01315 ( 2019 ). Minjie Wang, Da Zheng, Zihao Ye, Quan Gan, Mufei Li, Xiang Song, Jinjing Zhou, Chao Ma, Lingfan Yu, Yu Gai, Tianjun Xiao, Tong He, George Karypis, Jinyang Li, and Zheng Zhang. 2019. Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks. arXiv preprint arXiv:1909.01315 (2019)."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3448016.3457564"},{"key":"e_1_3_2_1_21_1","volume-title":"International Conference on Learning Representations. https:\/\/2.zoppoz.workers.dev:443\/https\/openreview.net\/forum?id=KYPz4YsCPj","author":"Wang Yanbang","year":"2021","unstructured":"Yanbang Wang , Yen-Yu Chang , Yunyu Liu , Jure Leskovec , and Pan Li . 2021 . Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks . In International Conference on Learning Representations. https:\/\/2.zoppoz.workers.dev:443\/https\/openreview.net\/forum?id=KYPz4YsCPj Yanbang Wang, Yen-Yu Chang, Yunyu Liu, Jure Leskovec, and Pan Li. 2021. Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks. In International Conference on Learning Representations. https:\/\/2.zoppoz.workers.dev:443\/https\/openreview.net\/forum?id=KYPz4YsCPj"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3572848.3577490"},{"key":"e_1_3_2_1_23_1","volume-title":"International Conference on Learning Representations.","author":"Xu Da","year":"2020","unstructured":"Da Xu , Chuanwei Ruan , Evren Korpeoglu , Sushant Kumar , and Kannan Achan . 2020 . Inductive representation learning on temporal graphs . In International Conference on Learning Representations. Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, and Kannan Achan. 2020. Inductive representation learning on temporal graphs. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539300"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.14778\/3514061.3514069"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/IA351965.2020.00011"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539177"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539352"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPS53621.2022.00111"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.14778\/3529337.3529342"}],"event":{"name":"SC '23: International Conference for High Performance Computing, Networking, Storage and Analysis","location":"Denver CO USA","acronym":"SC '23","sponsor":["SIGHPC ACM Special Interest Group on High Performance Computing, Special Interest Group on High Performance Computing","IEEE CS"]},"container-title":["Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis"],"original-title":[],"link":[{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/dl.acm.org\/doi\/10.1145\/3581784.3607056","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/dl.acm.org\/doi\/pdf\/10.1145\/3581784.3607056","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/dl.acm.org\/doi\/pdf\/10.1145\/3581784.3607056","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:36:23Z","timestamp":1750178183000},"score":1,"resource":{"primary":{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/dl.acm.org\/doi\/10.1145\/3581784.3607056"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,11]]},"references-count":30,"alternative-id":["10.1145\/3581784.3607056","10.1145\/3581784"],"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1145\/3581784.3607056","relation":{},"subject":[],"published":{"date-parts":[[2023,11,11]]},"assertion":[{"value":"2023-11-11","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}