{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T08:35:21Z","timestamp":1777106121529,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":53,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,8,24]],"date-time":"2024-08-24T00:00:00Z","timestamp":1724457600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Hong Kong RGC","award":["17207621, 17203522 and C7004-22G (CRF)"],"award-info":[{"award-number":["17207621, 17203522 and C7004-22G (CRF)"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,8,25]]},"DOI":"10.1145\/3637528.3671844","type":"proceedings-article","created":{"date-parts":[[2024,8,25]],"date-time":"2024-08-25T04:55:12Z","timestamp":1724561712000},"page":"2651-2662","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["MSPipe: Efficient Temporal GNN Training via Staleness-Aware Pipeline"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3395-3994","authenticated-orcid":false,"given":"Guangming","family":"Sheng","sequence":"first","affiliation":[{"name":"The University of Hong Kong, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0537-4004","authenticated-orcid":false,"given":"Junwei","family":"Su","sequence":"additional","affiliation":[{"name":"The University of Hong Kong, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2062-1512","authenticated-orcid":false,"given":"Chao","family":"Huang","sequence":"additional","affiliation":[{"name":"The University of Hong Kong, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3144-4398","authenticated-orcid":false,"given":"Chuan","family":"Wu","sequence":"additional","affiliation":[{"name":"The University of Hong Kong, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,8,24]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Gap aware mitigation of gradient staleness. arXiv preprint arXiv:1909.10802","author":"Barkai Saar","year":"2019","unstructured":"Saar Barkai, Ido Hakimi, and Assaf Schuster. 2019. Gap aware mitigation of gradient staleness. arXiv preprint arXiv:1909.10802 (2019)."},{"key":"e_1_3_2_2_2_1","volume-title":"Fastgcn: fast learning with graph convolutional networks via importance sampling. arXiv preprint arXiv:1801.10247","author":"Chen Jie","year":"2018","unstructured":"Jie Chen, Tengfei Ma, and Cao Xiao. 2018. Fastgcn: fast learning with graph convolutional networks via importance sampling. arXiv preprint arXiv:1801.10247 (2018)."},{"key":"e_1_3_2_2_3_1","volume-title":"Stochastic training of graph convolutional networks with variance reduction. arXiv preprint arXiv:1710.10568","author":"Chen Jianfei","year":"2017","unstructured":"Jianfei Chen, Jun Zhu, and Le Song. 2017. Stochastic training of graph convolutional networks with variance reduction. arXiv preprint arXiv:1710.10568 (2017)."},{"key":"e_1_3_2_2_4_1","unstructured":"Yangrui Chen Cong Xie Meng Ma Juncheng Gu Yanghua Peng Haibin Lin Chuan Wu and Yibo Zhu. 2022. SAPipe: Staleness-Aware Pipeline for Data Parallel DNN Training. In Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/HCS49909.2020.9220622"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403192"},{"key":"e_1_3_2_2_7_1","volume-title":"On the importance of sampling in learning graph convolutional networks. arXiv preprint arXiv:2103.02696","author":"Cong Weilin","year":"2021","unstructured":"Weilin Cong, Morteza Ramezani, and Mehrdad Mahdavi. 2021. On the importance of sampling in learning graph convolutional networks. arXiv preprint arXiv:2103.02696 (2021)."},{"key":"e_1_3_2_2_8_1","volume-title":"Do We Really Need Complicated Model Architectures For Temporal Networks? arXiv preprint arXiv:2302.11636","author":"Cong Weilin","year":"2023","unstructured":"Weilin Cong, Si Zhang, Jian Kang, Baichuan Yuan, Hao Wu, Xin Zhou, Hanghang Tong, and Mehrdad Mahdavi. 2023. Do We Really Need Complicated Model Architectures For Temporal Networks? arXiv preprint arXiv:2302.11636 (2023)."},{"key":"e_1_3_2_2_9_1","volume-title":"Toward Understanding the Impact of Staleness in Distributed Machine Learning. In International Conference on Learning Representations.","author":"Dai Wei","year":"2018","unstructured":"Wei Dai, Yi Zhou, Nanqing Dong, Hao Zhang, and Eric Xing. 2018. Toward Understanding the Impact of Staleness in Distributed Machine Learning. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_10_1","unstructured":"Swapnil Gandhi and Anand Padmanabha Iyer. 2021. P3: Distributed Deep Graph Learning at Scale.. In OSDI. 551--568."},{"key":"e_1_3_2_2_11_1","volume-title":"Inductive representation learning on large graphs. Advances in neural information processing systems","author":"Hamilton Will","year":"2017","unstructured":"Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. Advances in neural information processing systems, Vol. 30 (2017)."},{"key":"e_1_3_2_2_12_1","volume-title":"Phillip B Gibbons, Garth A Gibson, Greg Ganger, and Eric P Xing.","author":"Ho Qirong","year":"2013","unstructured":"Qirong Ho, James Cipar, Henggang Cui, Seunghak Lee, Jin Kyu Kim, Phillip B Gibbons, Garth A Gibson, Greg Ganger, and Eric P Xing. 2013. More effective distributed ml via a stale synchronous parallel parameter server. Advances in neural information processing systems, Vol. 26 (2013)."},{"key":"e_1_3_2_2_13_1","first-page":"172","article-title":"Accelerating training and inference of graph neural networks with fast sampling and pipelining","volume":"4","author":"Kaler Tim","year":"2022","unstructured":"Tim Kaler, Nickolas Stathas, Anne Ouyang, Alexandros-Stavros Iliopoulos, Tao Schardl, Charles E Leiserson, and Jie Chen. 2022. Accelerating training and inference of graph neural networks with fast sampling and pipelining. Proceedings of Machine Learning and Systems, Vol. 4 (2022), 172--189.","journal-title":"Proceedings of Machine Learning and Systems"},{"key":"e_1_3_2_2_14_1","volume-title":"Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907","author":"Kipf Thomas N","year":"2016","unstructured":"Thomas N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)."},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330895"},{"key":"e_1_3_2_2_16_1","volume-title":"Mining of massive data sets","author":"Leskovec Jure","unstructured":"Jure Leskovec, Anand Rajaraman, and Jeffrey David Ullman. 2020. Mining of massive data sets. Cambridge university press."},{"key":"e_1_3_2_2_17_1","volume-title":"Nam Sung Kim, and Alexander Schwing","author":"Li Youjie","year":"2018","unstructured":"Youjie Li, Mingchao Yu, Songze Li, Salman Avestimehr, Nam Sung Kim, and Alexander Schwing. 2018. Pipe-SGD: A decentralized pipelined SGD framework for distributed deep net training. Advances in Neural Information Processing Systems, Vol. 31 (2018)."},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403278"},{"key":"e_1_3_2_2_19_1","article-title":"Implementation and experimentation of producer-consumer synchronization problem","volume":"975","author":"Mehmood Syed Nasir","year":"2011","unstructured":"Syed Nasir Mehmood, Nazleeni Haron, Vaqar Akhtar, and Younus Javed. 2011. Implementation and experimentation of producer-consumer synchronization problem. International Journal of Computer Applications, Vol. 975, 8887 (2011), 32--37.","journal-title":"International Journal of Computer Applications"},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3184558.3191526"},{"key":"e_1_3_2_2_21_1","volume-title":"Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems","author":"Paszke Adam","year":"2019","unstructured":"Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems, Vol. 32 (2019)."},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.14778\/3538598.3538614"},{"key":"e_1_3_2_2_23_1","unstructured":"Farimah Poursafaei Shenyang Huang Kellin Pelrine and Reihaneh Rabbany. 2022. Towards Better Evaluation for Dynamic Link Prediction. In Neural Information Processing Systems (NeurIPS) Datasets and Benchmarks."},{"key":"e_1_3_2_2_24_1","volume-title":"A lock-free approach to parallelizing stochastic gradient descent. Advances in neural information processing systems","author":"Recht Benjamin","year":"2011","unstructured":"Benjamin Recht, Christopher Re, Stephen Wright, and Feng Niu. 2011. Hogwild!: A lock-free approach to parallelizing stochastic gradient descent. Advances in neural information processing systems, Vol. 24 (2011)."},{"key":"e_1_3_2_2_25_1","volume-title":"Proceedings of International Conference on Learning Representations.","author":"Rossi Emanuele","year":"2021","unstructured":"Emanuele Rossi, Ben Chamberlain, Fabrizio Frasca, Davide Eynard, Federico Monti, and Michael Bronstein. 2021. Temporal Graph Networks for Deep Learning on Dynamic Graphs. In Proceedings of International Conference on Learning Representations."},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3332295"},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371845"},{"key":"e_1_3_2_2_28_1","volume-title":"MSPipe: Efficient Temporal GNN Training via Staleness-aware Pipeline. arXiv preprint arXiv:2402.15113","author":"Sheng Guangming","year":"2024","unstructured":"Guangming Sheng, Junwei Su, Chao Huang, and Chuan Wu. 2024. MSPipe: Efficient Temporal GNN Training via Staleness-aware Pipeline. arXiv preprint arXiv:2402.15113 (2024)."},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3082932"},{"key":"e_1_3_2_2_30_1","volume-title":"BG-HGNN: Toward Scalable and Efficient Heterogeneous Graph Neural Network. arXiv preprint arXiv:2403.08207","author":"Su Junwei","year":"2024","unstructured":"Junwei Su, Lingjun Mao, and Chuan Wu. 2024. BG-HGNN: Toward Scalable and Efficient Heterogeneous Graph Neural Network. arXiv preprint arXiv:2403.08207 (2024)."},{"key":"e_1_3_2_2_31_1","volume-title":"Structure of Core-Periphery Communities. In International Conference on Complex Networks and Their Applications. Springer, 151--161","author":"Su Junwei","year":"2022","unstructured":"Junwei Su and Peter Marbach. 2022. Structure of Core-Periphery Communities. In International Conference on Complex Networks and Their Applications. Springer, 151--161."},{"key":"e_1_3_2_2_32_1","volume-title":"Towards robust inductive graph incremental learning via experience replay. arXiv preprint arXiv:2302.03534","author":"Su Junwei","year":"2023","unstructured":"Junwei Su and Chuan Wu. 2023. Towards robust inductive graph incremental learning via experience replay. arXiv preprint arXiv:2302.03534 (2023)."},{"key":"e_1_3_2_2_33_1","volume-title":"PRES: Toward Scalable Memory-Based Dynamic Graph Neural Networks. arXiv preprint arXiv:2402.04284","author":"Su Junwei","year":"2024","unstructured":"Junwei Su, Difan Zou, and Chuan Wu. 2024. PRES: Toward Scalable Memory-Based Dynamic Graph Neural Networks. arXiv preprint arXiv:2402.04284 (2024)."},{"key":"e_1_3_2_2_34_1","volume-title":"International Conference on Machine Learning. PMLR, 32728--32748","author":"Su Junwei","year":"2023","unstructured":"Junwei Su, Difan Zou, Zijun Zhang, and Chuan Wu. 2023. Towards robust graph incremental learning on evolving graphs. In International Conference on Machine Learning. PMLR, 32728--32748."},{"key":"e_1_3_2_2_35_1","volume-title":"International conference on learning representations.","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."},{"key":"e_1_3_2_2_36_1","volume-title":"MariusGNN: Resource-Efficient Out-of-Core Training of Graph Neural Networks. In Eighteenth European Conference on Computer Systems (EuroSys' 23)","author":"Waleffe Roger","year":"2023","unstructured":"Roger Waleffe, Jason Mohoney, Theodoros Rekatsinas, and Shivaram Venkataraman. 2023. MariusGNN: Resource-Efficient Out-of-Core Training of Graph Neural Networks. In Eighteenth European Conference on Computer Systems (EuroSys' 23)."},{"key":"e_1_3_2_2_37_1","volume-title":"PipeGCN: Efficient Full-Graph Training of Graph Convolutional Networks with Pipelined Feature Communication. In The Tenth International Conference on Learning Representations (ICLR","author":"Wan C","year":"2022","unstructured":"C Wan, Y Li, Cameron R Wolfe, A Kyrillidis, Nam S Kim, and Y Lin. 2022. PipeGCN: Efficient Full-Graph Training of Graph Convolutional Networks with Pipelined Feature Communication. In The Tenth International Conference on Learning Representations (ICLR 2022)."},{"key":"e_1_3_2_2_38_1","unstructured":"Minjie Wang Da Zheng Zihao Ye Quan Gan Mufei Li Xiang Song Jinjing Zhou Chao Ma Lingfan Yu Yu Gai et al. 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_2_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/3448016.3457564"},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3572848.3577490"},{"key":"e_1_3_2_2_41_1","volume-title":"Measuring temporal patterns in dynamic social networks. ACM Transactions on Knowledge Discovery from Data (TKDD)","author":"Wei Wei","year":"2015","unstructured":"Wei Wei and Kathleen M Carley. 2015. Measuring temporal patterns in dynamic social networks. ACM Transactions on Knowledge Discovery from Data (TKDD), Vol. 10, 1 (2015), 1--27."},{"key":"e_1_3_2_2_42_1","volume-title":"Inductive representation learning on temporal graphs. arXiv preprint arXiv:2002.07962","author":"Xu Da","year":"2020","unstructured":"Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, and Kannan Achan. 2020. Inductive representation learning on temporal graphs. arXiv preprint arXiv:2002.07962 (2020)."},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.12328"},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/3492321.3519557"},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401154"},{"key":"e_1_3_2_2_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219890"},{"key":"e_1_3_2_2_47_1","first-page":"1032","article-title":"IGE: A Framework for Learning Node Embeddings in Interaction Graphs","volume":"33","author":"Zhang Yao","year":"2019","unstructured":"Yao Zhang, Yun Xiong, Xiangnan Kong, Zhuang Niu, and Yangyong Zhu. 2019. IGE: A Framework for Learning Node Embeddings in Interaction Graphs. IEEE Transactions on Knowledge and Data Engineering, Vol. 33, 3 (2019), 1032--1044.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_2_2_48_1","volume-title":"TIGER: Temporal Interaction Graph Embedding with Restarts. arXiv preprint arXiv:2302.06057","author":"Zhang Yao","year":"2023","unstructured":"Yao Zhang, Yun Xiong, Yongxiang Liao, Yiheng Sun, Yucheng Jin, Xuehao Zheng, and Yangyong Zhu. 2023. TIGER: Temporal Interaction Graph Embedding with Restarts. arXiv preprint arXiv:2302.06057 (2023)."},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380076"},{"key":"e_1_3_2_2_50_1","doi-asserted-by":"publisher","DOI":"10.14778\/3514061.3514069"},{"key":"e_1_3_2_2_51_1","volume-title":"GNNFlow: A Distributed Framework for Continuous Temporal GNN Learning on Dynamic Graphs. arXiv preprint arXiv:2311.17410","author":"Zhong Yuchen","year":"2023","unstructured":"Yuchen Zhong, Guangming Sheng, Tianzuo Qin, Minjie Wang, Quan Gan, and Chuan Wu. 2023. GNNFlow: A Distributed Framework for Continuous Temporal GNN Learning on Dynamic Graphs. arXiv preprint arXiv:2311.17410 (2023)."},{"key":"e_1_3_2_2_52_1","volume-title":"Model-Architecture Co-Design for High Performance Temporal GNN Inference on FPGA. 2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS)","author":"Zhou Hongkuan","year":"2022","unstructured":"Hongkuan Zhou, Bingyi Zhang, Rajgopal Kannan, Viktor K. Prasanna, and Carl E. Busart. 2022. Model-Architecture Co-Design for High Performance Temporal GNN Inference on FPGA. 2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS) (2022), 1108--1117."},{"key":"e_1_3_2_2_53_1","volume-title":"Tgl: A general framework for temporal gnn training on billion-scale graphs. arXiv preprint arXiv:2203.14883","author":"Zhou Hongkuan","year":"2022","unstructured":"Hongkuan Zhou, Da Zheng, Israt Nisa, Vasileios Ioannidis, Xiang Song, and George Karypis. 2022. Tgl: A general framework for temporal gnn training on billion-scale graphs. arXiv preprint arXiv:2203.14883 (2022)."}],"event":{"name":"KDD '24: The 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Barcelona Spain","acronym":"KDD '24","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3671844","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3637528.3671844","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:04:14Z","timestamp":1750291454000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3671844"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,24]]},"references-count":53,"alternative-id":["10.1145\/3637528.3671844","10.1145\/3637528"],"URL":"https:\/\/doi.org\/10.1145\/3637528.3671844","relation":{},"subject":[],"published":{"date-parts":[[2024,8,24]]},"assertion":[{"value":"2024-08-24","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}