{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T17:36:22Z","timestamp":1780335382789,"version":"3.54.1"},"publisher-location":"New York, NY, USA","reference-count":31,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,11,13]],"date-time":"2021-11-13T00:00:00Z","timestamp":1636761600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,11,14]]},"DOI":"10.1145\/3458817.3480858","type":"proceedings-article","created":{"date-parts":[[2021,10,21]],"date-time":"2021-10-21T05:10:34Z","timestamp":1634793034000},"page":"1-15","source":"Crossref","is-referenced-by-count":34,"title":["Efficient scaling of dynamic graph neural networks"],"prefix":"10.1145","author":[{"given":"Venkatesan T.","family":"Chakaravarthy","sequence":"first","affiliation":[{"name":"IBM Research, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shivmaran S.","family":"Pandian","sequence":"additional","affiliation":[{"name":"IBM Research, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saurabh","family":"Raje","sequence":"additional","affiliation":[{"name":"IBM Research, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yogish","family":"Sabharwal","sequence":"additional","affiliation":[{"name":"IBM Research, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Toyotaro","family":"Suzumura","sequence":"additional","affiliation":[{"name":"IBM T.J. Watson Research Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shashanka","family":"Ubaru","sequence":"additional","affiliation":[{"name":"IBM T.J. Watson Research Center"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,11,13]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Computing graph neural networks: A survey from algorithms to accelerators. arXiv preprint arXiv:2010.00130","author":"Abadal Sergi","year":"2020"},{"key":"e_1_3_2_2_2_1","volume-title":"PaToH: A multilevel hypergraph partitioning tool, version 3.0","author":"Cataly\u00fcrek Umit V","year":"1999"},{"key":"e_1_3_2_2_3_1","volume-title":"GC-LSTM: Graph convolution embedded LSTM for dynamic link prediction. arXiv preprint arXiv:1812.04206","author":"Chen Jinyin","year":"2018"},{"key":"e_1_3_2_2_4_1","volume-title":"Training deep nets with sublinear memory cost. arXiv preprint arXiv:1604.06174","author":"Chen Tianqi","year":"2016"},{"key":"e_1_3_2_2_5_1","volume-title":"Fast graph representation learning with PyTorch Geometric. arXiv preprint arXiv:1903.02428","author":"Fey Matthias","year":"2019"},{"key":"e_1_3_2_2_6_1","first-page":"4125","article-title":"Memory-efficient backpropagation through time","volume":"29","author":"Gruslys Audrunas","year":"2016","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_7_1","volume-title":"Long short-term memory. Neural computation 9, 8","author":"Hochreiter Sepp","year":"1997"},{"key":"e_1_3_2_2_8_1","first-page":"187","article-title":"Improving the accuracy, scalability, and performance of graph neural networks with ROC","volume":"2","author":"Jia Zhihao","year":"2020","journal-title":"Proceedings of Machine Learning and Systems"},{"key":"e_1_3_2_2_9_1","first-page":"1","article-title":"Representation learning for dynamic Graphs: A survey","volume":"21","author":"Kazemi Seyed Mehran","year":"2020","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1016\/j.laa.2015.07.021","article-title":"Tensor-tensor products with invertible linear transforms","volume":"485","author":"Kernfeld Eric","year":"2015","journal-title":"Linear Algebra Appl."},{"key":"e_1_3_2_2_11_1","volume-title":"5th International Conference on Learning Representations, ICLR","author":"Thomas","year":"2017"},{"key":"e_1_3_2_2_12_1","volume-title":"Learning dynamic embeddings from temporal interactions. arXiv preprint arXiv:1812.02289","author":"Kumar Srijan","year":"2018"},{"key":"e_1_3_2_2_13_1","volume-title":"Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. arXiv preprint arXiv:1707.01926","author":"Li Yaguang","year":"2017"},{"key":"e_1_3_2_2_14_1","volume-title":"2019 USENIX Annual Technical Conference. 443--458","author":"Ma Lingxiao","year":"2019"},{"key":"e_1_3_2_2_15_1","volume-title":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 719--728","author":"Ma Yao","year":"2020"},{"key":"e_1_3_2_2_16_1","volume-title":"Proceedings of the 2021 SIAM International Conference on Data Mining (SDM). 729--737","author":"Malik Osman Asif","year":"2021"},{"key":"e_1_3_2_2_17_1","volume-title":"Dynamic graph convolutional networks. Pattern Recognition 97","author":"Manessi Franco","year":"2020"},{"key":"e_1_3_2_2_18_1","volume-title":"2018 IEEE International Conference on Big Data. 1085--1092","author":"Nguyen Giang H","year":"2018"},{"key":"e_1_3_2_2_19_1","volume-title":"Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence.","author":"Pareja Aldo"},{"key":"e_1_3_2_2_20_1","volume-title":"Temporal graph networks for deep learning on dynamic graphs. arXiv preprint arXiv:2006.10637","author":"Rossi Emanuele","year":"2020"},{"key":"e_1_3_2_2_21_1","volume-title":"Twenty-Ninth AAAI Conference on Artificial Intelligence. http:\/\/networkrepository.com","author":"Rossi Ryan","year":"2015"},{"key":"e_1_3_2_2_22_1","volume-title":"International Conference on Neural Information Processing. Springer, 362--373","author":"Seo Youngjoo","year":"2018"},{"key":"e_1_3_2_2_23_1","volume-title":"SC20: International Conference for High Performance Computing, Networking, Storage and Analysis. IEEE, 1--14","author":"Tripathy Alok","year":"2020"},{"key":"e_1_3_2_2_24_1","volume-title":"International Conference on Machine Learning. PMLR, 3462--3471","author":"Trivedi Rakshit","year":"2017"},{"key":"e_1_3_2_2_25_1","unstructured":"Minjie Wang Lingfan Yu Da Zheng Quan Gan Yu Gai Zihao Ye Mufei Li Jinjing Zhou Qi Huang Chao Ma etal 2019. Deep Graph Library: Towards efficient and scalable deep learning on graphs. (2019). https:\/\/www.dgl.ai\/  Minjie Wang Lingfan Yu Da Zheng Quan Gan Yu Gai Zihao Ye Mufei Li Jinjing Zhou Qi Huang Chao Ma et al. 2019. Deep Graph Library: Towards efficient and scalable deep learning on graphs. (2019). https:\/\/www.dgl.ai\/"},{"key":"e_1_3_2_2_26_1","volume-title":"Scalable graph learning for anti-money laundering: A first look. arXiv preprint arXiv:1812.00076","author":"Weber Mark","year":"2018"},{"key":"e_1_3_2_2_27_1","first-page":"4","article-title":"A comprehensive survey on graph neural networks","volume":"32","author":"Wu Zonghan","year":"2020","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"crossref","first-page":"1588","DOI":"10.1109\/TPDS.2020.2970047","article-title":"Distributed graph computation meets machine learning","volume":"31","author":"Xiao Wencong","year":"2020","journal-title":"IEEE Transactions on Parallel and Distributed Systems"},{"key":"e_1_3_2_2_29_1","volume-title":"Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 974--983","author":"Ying Rex","year":"2018"},{"key":"e_1_3_2_2_30_1","volume-title":"AGL: A scalable system for industrial-purpose graph machine learning. arXiv preprint arXiv:2003.02454","author":"Zhang Dalong","year":"2020"},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"crossref","first-page":"2094","DOI":"10.14778\/3352063.3352127","article-title":"AliGraph: A comprehensive graph neural network platform","volume":"12","author":"Zhu Rong","year":"2019","journal-title":"Proceedings VLDB Endowment"}],"event":{"name":"SC '21: The International Conference for High Performance Computing, Networking, Storage and Analysis","location":"St. Louis Missouri","acronym":"SC '21","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:\/\/dl.acm.org\/doi\/10.1145\/3458817.3480858","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3458817.3480858","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:12:22Z","timestamp":1750191142000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3458817.3480858"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,13]]},"references-count":31,"alternative-id":["10.1145\/3458817.3480858","10.1145\/3458817"],"URL":"https:\/\/doi.org\/10.1145\/3458817.3480858","relation":{},"subject":[],"published":{"date-parts":[[2021,11,13]]}}}