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Database Syst."],"published-print":{"date-parts":[[2025,3,31]]},"abstract":"<jats:p>\n            Representation learning over dynamic graphs is critical for many real-world applications such as social network services and recommender systems. Temporal graph neural networks (T-GNNs) are powerful representation learning methods and have demonstrated remarkable effectiveness on continuous-time dynamic graphs. However, T-GNNs still suffer from high time complexity, which increases linearly with the number of timestamps and grows exponentially with the model depth, making them not scalable to large dynamic graphs. To address the limitations, we propose\n            <jats:sans-serif>Orca<\/jats:sans-serif>\n            , a novel framework that accelerates T-GNN training by caching and reusing intermediate embeddings. We design an optimal caching policy, named\n            <jats:sans-serif>MRD<\/jats:sans-serif>\n            , for the uniform cache replacement problem, where embeddings at different intermediate layers have identical dimensions and recomputation costs.\n            <jats:sans-serif>MRD<\/jats:sans-serif>\n            not only improves the efficiency of training T-GNNs by maximizing the number of cache hits but also reduces the approximation errors by avoiding keeping and reusing extremely stale embeddings. For the general cache replacement problem, where embeddings at different intermediate layers can have different dimensions and recomputation costs, we solve this NP-hard problem by presenting a novel two-stage framework with approximation guarantees on the achieved benefit of caching. Furthermore, we have developed profound theoretical analyses of the approximation errors introduced by reusing intermediate embeddings, providing a thorough understanding of the impact of our caching and reuse schemes on model outputs. We also offer rigorous convergence guarantees for model training, adding to the reliability and validity of our\n            <jats:sans-serif>Orca<\/jats:sans-serif>\n            framework. Extensive experiments have validated that\n            <jats:sans-serif>Orca<\/jats:sans-serif>\n            can obtain two orders of magnitude speedup over state-of-the-art T-GNNs while achieving higher precision on various dynamic graphs.\n          <\/jats:p>","DOI":"10.1145\/3705894","type":"journal-article","created":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T10:53:54Z","timestamp":1732532034000},"page":"1-46","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A Caching-based Framework for Scalable Temporal Graph Neural Network Training"],"prefix":"10.1145","volume":"50","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8376-6244","authenticated-orcid":false,"given":"Yiming","family":"Li","sequence":"first","affiliation":[{"name":"The Hong Kong University of Science and Technology, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8364-3674","authenticated-orcid":false,"given":"Yanyan","family":"Shen","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8257-5806","authenticated-orcid":false,"given":"Lei","family":"Chen","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China and The Hong Kong University of Science and Technology, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2236-8784","authenticated-orcid":false,"given":"Mingxuan","family":"Yuan","sequence":"additional","affiliation":[{"name":"Huawei Technologies Noah's Ark Lab Hong Kong, Hong Kong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,1,10]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"2024. 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IEEE, 802\u2013814."},{"key":"e_1_3_2_7_2","first-page":"31","volume-title":"Proceedings of the 10th annual ACM-SIAM symposium on Discrete algorithms","author":"Albers Susanne","year":"1999","unstructured":"Susanne Albers, Sanjeev Arora, and Sanjeev Khanna. 1999. Page replacement for general caching problems. In Proceedings of the 10th annual ACM-SIAM symposium on Discrete algorithms. ACM\/SIAM, 31\u201340."},{"key":"e_1_3_2_8_2","unstructured":"Raghu Arghal Eric Lei and Shirin Saeedi Bidokhti. 2022. Robust graph neural networks via probabilistic lipschitz constraints. In Learning for Dynamics and Control Conference L4DC 2022 23-24 June 2022 Stanford University Stanford CA USA (Proceedings of Machine Learning Research) Roya Firoozi Negar Mehr Esen Yel Rika Antonova Jeannette Bohg Mac Schwager and Mykel J. Kochenderfer (Eds.). Vol. 168 PMLR 1073\u20131085. Retrieved from https:\/\/proceedings.mlr.press\/v168\/arghal22a.html"},{"key":"e_1_3_2_9_2","first-page":"735","volume-title":"Proceedings of the 32nd annual ACM symposium on Theory of computing","author":"Bar-Noy Amotz","year":"2000","unstructured":"Amotz Bar-Noy, Reuven Bar-Yehuda, Ari Freund, Joseph Naor, and Baruch Schieber. 2000. A unified approach to approximating resource allocation and scheduling. In Proceedings of the 32nd annual ACM symposium on Theory of computing. ACM, 735\u2013744."},{"issue":"2","key":"e_1_3_2_10_2","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1147\/sj.52.0078","article-title":"A study of replacement algorithms for virtual-storage computer","volume":"5","author":"Belady Laszlo A.","year":"1966","unstructured":"Laszlo A. Belady. 1966. A study of replacement algorithms for virtual-storage computer. IBM Systems Journal 5, 2 (1966), 78\u2013101.","journal-title":"IBM Systems Journal"},{"key":"e_1_3_2_11_2","volume-title":"Proceedings of the Conference on Innovative Data Systems Research","author":"Bhardwaj Anant P.","year":"2015","unstructured":"Anant P. Bhardwaj, Souvik Bhattacherjee, Amit Chavan, Amol Deshpande, Aaron J. Elmore, Samuel Madden, and Aditya G. Parameswaran. 2015. DataHub: Collaborative data science & dataset version management at scale. In Proceedings of the Conference on Innovative Data Systems Research. Retrieved from www.cidrdb.org"},{"issue":"12","key":"e_1_3_2_12_2","first-page":"1346","article-title":"Principles of dataset versioning: Exploring the recreation\/storage tradeoff","volume":"8","author":"Bhattacherjee Souvik","year":"2015","unstructured":"Souvik Bhattacherjee, Amit Chavan, Silu Huang, Amol Deshpande, and Aditya G. Parameswaran. 2015. Principles of dataset versioning: Exploring the recreation\/storage tradeoff. PVLDB 8, 12 (2015), 1346\u20131357.","journal-title":"PVLDB"},{"key":"e_1_3_2_13_2","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-031-01869-5","volume-title":"Data Management in Machine Learning Systems","author":"Boehm Matthias","year":"2019","unstructured":"Matthias Boehm, Arun Kumar, and Jun Yang. 2019. Data Management in Machine Learning Systems. Morgan & Claypool Publishers."},{"key":"e_1_3_2_14_2","first-page":"941","volume-title":"Proceedings of the International Conference on Machine Learning","volume":"80","author":"Chen Jianfei","year":"2018","unstructured":"Jianfei Chen, Jun Zhu, and Le Song. 2018. Stochastic training of graph convolutional networks with variance reduction. In Proceedings of the International Conference on Machine Learning, Vol. 80. PMLR, 941\u2013949."},{"issue":"4","key":"e_1_3_2_15_2","first-page":"295","article-title":"Materialized Views","volume":"4","author":"Chirkova Rada","year":"2012","unstructured":"Rada Chirkova and Jun Yang. 2012. Materialized Views. Foundations and Trends in Databases (TODS) 4, 4 (2012), 295\u2013405.","journal-title":"Foundations and Trends in Databases (TODS)"},{"key":"e_1_3_2_16_2","first-page":"78","volume-title":"Proceedings of the PODS","author":"Chrobak Marek","year":"1998","unstructured":"Marek Chrobak and John Noga. 1998. LRU is better than FIFO. In Proceedings of the PODS. ACM\/SIAM, 78\u201381."},{"issue":"4","key":"e_1_3_2_17_2","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1007\/s00453-011-9502-9","article-title":"Caching Is hard - even in the fault model","volume":"63","author":"Chrobak Marek","year":"2012","unstructured":"Marek Chrobak, Gerhard J. Woeginger, Kazuhisa Makino, and Haifeng Xu. 2012. Caching Is hard - even in the fault model. 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Retrieved from https:\/\/openreview.net\/forum?id=ayPPc0SyLv1"},{"key":"e_1_3_2_20_2","first-page":"613","volume-title":"Proceedings of the 14th USENIX Conference on Networked Systems Design and Implementation","author":"Crankshaw Daniel","year":"2017","unstructured":"Daniel Crankshaw, Xin Wang, Giulio Zhou, Michael J. Franklin, Joseph E. Gonzalez, and Ion Stoica. 2017. Clipper: A low-latency online prediction serving system. In Proceedings of the 14th USENIX Conference on Networked Systems Design and Implementation. USENIX Association, 613\u2013627."},{"key":"e_1_3_2_21_2","doi-asserted-by":"crossref","first-page":"1701","DOI":"10.1145\/3318464.3389715","volume-title":"Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data","author":"Derakhshan Behrouz","year":"2020","unstructured":"Behrouz Derakhshan, Alireza Rezaei Mahdiraji, Ziawasch Abedjan, Tilmann Rabl, and Volker Markl. 2020. Optimizing machine learning workloads in collaborative environments. 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In Proceedings of the International Conference on Machine Learning, Vol. 139. 3294\u20133304."},{"issue":"2","key":"e_1_3_2_24_2","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1007\/s00453-016-0185-0","article-title":"General caching is hard: Even with small pages","volume":"79","author":"Folwarczn\u00fd Luk\u00e1\u0161","year":"2017","unstructured":"Luk\u00e1\u0161 Folwarczn\u00fd and Jir\u00ed Sgall. 2017. General caching is hard: Even with small pages. Algorithmica 79, 2 (2017), 319\u2013339.","journal-title":"Algorithmica"},{"issue":"1","key":"e_1_3_2_25_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0377-2217(03)00274-1","article-title":"The multidimensional 0-1 knapsack problem: An overview","volume":"155","author":"Fr\u00e9ville Arnaud","year":"2004","unstructured":"Arnaud Fr\u00e9ville. 2004. The multidimensional 0-1 knapsack problem: An overview. European Journal of Operational Research 155, 1 (2004), 1\u201321.","journal-title":"European Journal of Operational Research"},{"key":"e_1_3_2_26_2","doi-asserted-by":"crossref","first-page":"5680","DOI":"10.1109\/TSP.2020.3026980","article-title":"Stability properties of graph neural networks","volume":"68","author":"Gama Fernando","year":"2020","unstructured":"Fernando Gama, Joan Bruna, and Alejandro Ribeiro. 2020. Stability properties of graph neural networks. IEEE Transactions on Signal Process. 68 (2020), 5680\u20135695.","journal-title":"IEEE Transactions on Signal Process."},{"issue":"5","key":"e_1_3_2_27_2","first-page":"1060","article-title":"ETC: Efficient training of temporal graph neural networks over large-scale dynamic graphs","volume":"17","author":"Gao Shihong","year":"2024","unstructured":"Shihong Gao, Yiming Li, Yanyan Shen, Yingxia Shao, and Lei Chen. 2024. ETC: Efficient training of temporal graph neural networks over large-scale dynamic graphs. PVLDB 17, 5 (2024), 1060\u20131072.","journal-title":"PVLDB"},{"issue":"3","key":"e_1_3_2_28_2","first-page":"174","article-title":"SIMPLE: Efficient temporal graph neural network training at scale with dynamic data placement","volume":"2","author":"Gao Shihong","year":"2024","unstructured":"Shihong Gao, Yiming Li, Xin Zhang, Yanyan Shen, Yingxia Shao, and Lei Chen. 2024. SIMPLE: Efficient temporal graph neural network training at scale with dynamic data placement. Proceedings of the ACM on Management of Data 2, 3 (2024), 174.","journal-title":"Proceedings of the ACM on Management of Data"},{"issue":"2","key":"e_1_3_2_29_2","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1162\/neco.1995.7.2.219","article-title":"Regularization theory and neural networks architectures","volume":"7","author":"Girosi Federico","year":"1995","unstructured":"Federico Girosi, Michael J. Jones, and Tomaso A. Poggio. 1995. Regularization theory and neural networks architectures. Neural Computation 7, 2 (1995), 219\u2013269.","journal-title":"Neural Computation"},{"key":"e_1_3_2_30_2","article-title":"dyngraph2vec: Capturing network dynamics using dynamic graph representation learning","volume":"187","author":"Goyal Palash","year":"2020","unstructured":"Palash Goyal, Sujit Rokka Chhetri, and Arquimedes Canedo. 2020. dyngraph2vec: Capturing network dynamics using dynamic graph representation learning. Knowledge Based System 187 (2020).","journal-title":"Knowledge Based System"},{"key":"e_1_3_2_31_2","unstructured":"Palash Goyal Sujit Rokka Chhetri Ninareh Mehrabi Emilio Ferrara and Arquimedes Canedo. 2018. DynamicGEM: A library for dynamic graph embedding methods. arXiv:1811.10734. Retrieved from https:\/\/arxiv.org\/abs\/1811.10734"},{"key":"e_1_3_2_32_2","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1145\/2939672.2939754","volume-title":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Grover Aditya","year":"2016","unstructured":"Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 855\u2013864."},{"key":"e_1_3_2_33_2","first-page":"10700","volume-title":"Proceedings of the 33rd International Conference on Neural Information Processing Systems","author":"Hajiramezanali Ehsan","year":"2019","unstructured":"Ehsan Hajiramezanali, Arman Hasanzadeh, Krishna R. Narayanan, Nick Duffield, Mingyuan Zhou, and Xiaoning Qian. 2019. Variational graph recurrent neural networks. In Proceedings of the 33rd International Conference on Neural Information Processing Systems. 10700\u201310710."},{"issue":"3","key":"e_1_3_2_34_2","first-page":"52","article-title":"Representation learning on graphs: Methods and applications","volume":"40","author":"Hamilton William L.","year":"2017","unstructured":"William L. Hamilton, Rex Ying, and Jure Leskovec. 2017. Representation learning on graphs: Methods and applications. IEEE Data Engineering Bulletin 40, 3 (2017), 52\u201374.","journal-title":"IEEE Data Engineering Bulletin"},{"key":"e_1_3_2_35_2","first-page":"1024","volume-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems","author":"Hamilton William L.","year":"2017","unstructured":"William L. Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Proceedings of the 31st International Conference on Neural Information Processing Systems. 1024\u20131034."},{"key":"e_1_3_2_36_2","first-page":"580","volume-title":"Proceedings of the 32nd International Conference on Neural Information Processing Systems","author":"Hanin Boris","year":"2018","unstructured":"Boris Hanin. 2018. Which neural net architectures give rise to exploding and vanishing gradients?. In Proceedings of the 32nd International Conference on Neural Information Processing Systems. 580\u2013589."},{"key":"e_1_3_2_37_2","first-page":"440","volume-title":"Proceedings of the SIGMOD","author":"Hanson Eric N.","year":"1987","unstructured":"Eric N. Hanson. 1987. A performance analysis of view materialization strategies. In Proceedings of the SIGMOD. ACM Press, 440\u2013453."},{"issue":"3","key":"e_1_3_2_38_2","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1007\/s00453-001-0125-4","article-title":"Page replacement with multi-size pages and applications to web caching","volume":"33","author":"Irani Sandy","year":"2002","unstructured":"Sandy Irani. 2002. Page replacement with multi-size pages and applications to web caching. Algorithmica 33, 3 (2002), 384\u2013409.","journal-title":"Algorithmica"},{"issue":"6","key":"e_1_3_2_39_2","doi-asserted-by":"crossref","first-page":"1424","DOI":"10.1109\/72.548170","article-title":"An analysis of noise in recurrent neural networks: Convergence and generalization","volume":"7","author":"Jim Kam-Chuen","year":"1996","unstructured":"Kam-Chuen Jim, C. Lee Giles, and Bill G. Horne. 1996. An analysis of noise in recurrent neural networks: Convergence and generalization. IEEE Transactions on Neural Networks 7, 6 (1996), 1424\u20131438.","journal-title":"IEEE Transactions on Neural Networks"},{"key":"e_1_3_2_40_2","first-page":"439","volume-title":"Proceedings of the 20th International Conference on Very Large Data Bases","author":"Johnson Theodore","year":"1994","unstructured":"Theodore Johnson and Dennis E. Shasha. 1994. 2Q: A low overhead high performance buffer management replacement algorithm. In Proceedings of the 20th International Conference on Very Large Data Bases. Morgan Kaufmann, 439\u2013450."},{"issue":"11","key":"e_1_3_2_41_2","first-page":"1586","article-title":"NoScope: Optimizing deep CNN-based queries over video streams at scale","volume":"10","author":"Kang Daniel","year":"2017","unstructured":"Daniel Kang, John Emmons, Firas Abuzaid, Peter Bailis, and Matei Zaharia. 2017. NoScope: Optimizing deep CNN-based queries over video streams at scale. PVLDB 10, 11 (2017), 1586\u20131597.","journal-title":"PVLDB"},{"key":"e_1_3_2_42_2","unstructured":"Thomas N. Kipf and Max Welling. 2016. Variational graph auto-encoders. arXiv:1611.07308. Retrieved from https:\/\/arxiv.org\/abs\/1611.07308"},{"key":"e_1_3_2_43_2","doi-asserted-by":"crossref","first-page":"1269","DOI":"10.1145\/3292500.3330895","volume-title":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","author":"Kumar Srijan","year":"2019","unstructured":"Srijan Kumar, Xikun Zhang, and Jure Leskovec. 2019. Predicting dynamic embedding trajectory in temporal interaction networks. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 1269\u20131278."},{"key":"e_1_3_2_44_2","first-page":"611","volume-title":"Proceedings of the 13th USENIX Conference on Operating Systems Design and Implementation","author":"Lee Yunseong","year":"2018","unstructured":"Yunseong Lee, Alberto Scolari, Byung-Gon Chun, Marco Domenico Santambrogio, Markus Weimer, and Matteo Interlandi. 2018. PRETZEL: Opening the black box of machine learning prediction serving systems. In Proceedings of the 13th USENIX Conference on Operating Systems Design and Implementation. USENIX Association, 611\u2013626."},{"key":"e_1_3_2_45_2","first-page":"937","volume-title":"Proceedings of the 30th ACM International Conference on Information & Knowledge Management","author":"Li Haoyang","year":"2021","unstructured":"Haoyang Li and Lei Chen. 2021. Cache-based GNN system for dynamic graphs. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. ACM, 937\u2013946."},{"key":"e_1_3_2_46_2","first-page":"1271","volume-title":"Proceedings of the 2022 International Conference on Management of Data","author":"Li Yiming","year":"2022","unstructured":"Yiming Li, Yanyan Shen, and Lei Chen. 2022. Camel: Managing data for efficient stream learning. In Proceedings of the 2022 International Conference on Management of Data. ACM, 1271\u20131285."},{"issue":"1","key":"e_1_3_2_47_2","first-page":"52:1\u201352:27","article-title":"Orca: Scalable temporal graph neural network training with theoretical guarantees","volume":"1","author":"Li Yiming","year":"2023","unstructured":"Yiming Li, Yanyan Shen, Lei Chen, and Mingxuan Yuan. 2023. Orca: Scalable temporal graph neural network training with theoretical guarantees. 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AAAI Press, 8510\u20138517."},{"key":"e_1_3_2_50_2","doi-asserted-by":"crossref","first-page":"731","DOI":"10.1145\/3318464.3386126","volume-title":"Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data","author":"Liberty Edo","year":"2020","unstructured":"Edo Liberty, Zohar S. Karnin, Bing Xiang, Laurence Rouesnel, Baris Coskun, Ramesh Nallapati, Julio Delgado, Amir Sadoughi, Yury Astashonok, Piali Das, Can Balioglu, Saswata Chakravarty, Madhav Jha, Philip Gautier, David Arpin, Tim Januschowski, Valentin Flunkert, Yuyang Wang, Jan Gasthaus, Lorenzo Stella, Syama Sundar Rangapuram, David Salinas, Sebastian Schelter, and Alex Smola. 2020. Elastic machine learning algorithms in amazon sagemaker. In Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data. ACM, 731\u2013737."},{"key":"e_1_3_2_51_2","first-page":"779","volume-title":"Proceedings of the 14th ACM International Conference on Web Search and Data Mining","author":"Luo Dongsheng","year":"2021","unstructured":"Dongsheng Luo, Wei Cheng, Wenchao Yu, Bo Zong, Jingchao Ni, Haifeng Chen, and Xiang Zhang. 2021. Learning to drop: Robust graph neural network via topological denoising. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining. ACM, 779\u2013787."},{"key":"e_1_3_2_52_2","first-page":"2767","volume-title":"Proceedings of the International Joint Conference on Artificial Intelligence","author":"Luo Gongxu","year":"2021","unstructured":"Gongxu Luo, Jianxin Li, Hao Peng, Carl J. Yang, Lichao Sun, Philip S. Yu, and Lifang He. 2021. Graph entropy guided node embedding dimension selection for graph neural networks. In Proceedings of the International Joint Conference on Artificial Intelligence. Retrieved from ijcai.org, 2767\u20132774."},{"key":"e_1_3_2_53_2","series-title":"Proceedings of Machine Learning Research","first-page":"1","volume-title":"Proceedings of the LOG","volume":"198","author":"Luo Yuhong","year":"2022","unstructured":"Yuhong Luo and Pan Li. 2022. Neighborhood-aware scalable temporal network representation learning. In Proceedings of the LOG(Proceedings of Machine Learning Research, Vol. 198). PMLR, 1."},{"key":"e_1_3_2_54_2","first-page":"3762","volume-title":"Proceedings of the 2018 IEEE International Conference on Big Data (Big Data)","author":"Mahdavi Sedigheh","year":"2018","unstructured":"Sedigheh Mahdavi, Shima Khoshraftar, and Aijun An. 2018. dynnode2vec: Scalable dynamic network embedding. In Proceedings of the 2018 IEEE International Conference on Big Data (Big Data). IEEE, 3762\u20133765."},{"issue":"1","key":"e_1_3_2_55_2","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1145\/2206869.2206874","article-title":"A survey of view selection methods","volume":"41","author":"Mami Imene","year":"2012","unstructured":"Imene Mami and Zohra Bellahsene. 2012. A survey of view selection methods. SIGMOD Record 41, 1 (2012), 20\u201329.","journal-title":"SIGMOD Record"},{"key":"e_1_3_2_56_2","first-page":"1393","volume-title":"Proceedings of the IEEE International Conference on Data Engineering","author":"Miao Hui","year":"2017","unstructured":"Hui Miao, Ang Li, Larry S. Davis, and Amol Deshpande. 2017. ModelHub: Deep learning lifecycle management. In Proceedings of the IEEE International Conference on Data Engineering. IEEE Computer Society, 1393\u20131394."},{"issue":"2","key":"e_1_3_2_57_2","first-page":"312","article-title":"HET: Scaling out huge embedding model training via cache-enabled distributed framework","volume":"15","author":"Miao Xupeng","year":"2021","unstructured":"Xupeng Miao, Hailin Zhang, Yining Shi, Xiaonan Nie, Zhi Yang, Yangyu Tao, and Bin Cui. 2021. HET: Scaling out huge embedding model training via cache-enabled distributed framework. PVLDB 15, 2 (2021), 312\u2013320.","journal-title":"PVLDB"},{"issue":"4","key":"e_1_3_2_58_2","first-page":"50:1\u201350:19","article-title":"Some mathematical facts about optimal cache replacement","volume":"13","author":"Michaud Pierre","year":"2016","unstructured":"Pierre Michaud. 2016. Some mathematical facts about optimal cache replacement. ACM Transactions on Architecture and Code Optimization 13, 4 (2016), 50:1\u201350:19.","journal-title":"ACM Transactions on Architecture and Code Optimization"},{"key":"e_1_3_2_59_2","doi-asserted-by":"crossref","first-page":"1685","DOI":"10.1145\/3318464.3389709","volume-title":"Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data","author":"Nakandala Supun","year":"2020","unstructured":"Supun Nakandala and Arun Kumar. 2020. Vista: Optimized system for declarative feature transfer from deep CNNs at scale. In Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data. ACM, 1685\u20131700."},{"key":"e_1_3_2_60_2","doi-asserted-by":"crossref","first-page":"506","DOI":"10.1145\/3514221.3517846","volume-title":"Proceedings of the 2022 International Conference on Management of Data","author":"Nakandala Supun","year":"2022","unstructured":"Supun Nakandala and Arun Kumar. 2022. Nautilus: An optimized system for deep transfer learning over evolving training datasets. In Proceedings of the 2022 International Conference on Management of Data. ACM, 506\u2013520."},{"key":"e_1_3_2_61_2","unstructured":"S. Deepak Narayanan Aditya Sinha Prateek Jain Purushottam Kar and Sundararajan Sellamanickam. 2022. IGLU: Efficient GCN training via lazy updates. In The Tenth International Conference on Learning Representations ICLR 2022 Virtual Event April 25-29 2022 OpenReview.net. Retrieved from https:\/\/openreview.net\/forum?id=5kq11Tl1z4"},{"issue":"4","key":"e_1_3_2_62_2","doi-asserted-by":"crossref","first-page":"692","DOI":"10.26599\/TST.2021.9010057","article-title":"Graph neural architecture search: A survey","volume":"27","author":"Oloulade Babatounde Moctard","year":"2021","unstructured":"Babatounde Moctard Oloulade, Jianliang Gao, Jiamin Chen, Tengfei Lyu, and Raeed Al-Sabri. 2021. Graph neural architecture search: A survey. 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SANCUS: Staleness-aware communication-avoiding full-graph decentralized training in large-scale graph neural networks. PVLDB 15, 9 (2022), 1937\u20131950.","journal-title":"PVLDB"},{"key":"e_1_3_2_65_2","doi-asserted-by":"crossref","first-page":"701","DOI":"10.1145\/2623330.2623732","volume-title":"Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Perozzi Bryan","year":"2014","unstructured":"Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. 2014. DeepWalk: online learning of social representations. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 701\u2013710."},{"key":"e_1_3_2_66_2","doi-asserted-by":"crossref","first-page":"1426","DOI":"10.1145\/3448016.3452788","volume-title":"Proceedings of the 2021 International Conference on Management of Data","author":"Phani Arnab","year":"2021","unstructured":"Arnab Phani, Benjamin Rath, and Matthias Boehm. 2021. LIMA: Fine-grained lineage tracing and reuse in machine learning systems. In Proceedings of the 2021 International Conference on Management of Data. ACM, 1426\u20131439."},{"key":"e_1_3_2_67_2","first-page":"16860","volume-title":"Proceedings of the 35th International Conference on Neural Information Processing Systems","author":"Qin Yijian","year":"2021","unstructured":"Yijian Qin, Xin Wang, Zeyang Zhang, and Wenwu Zhu. 2021. Graph differentiable architecture search with structure learning. In Proceedings of the 35th International Conference on Neural Information Processing Systems. 16860\u201316872."},{"issue":"1","key":"e_1_3_2_68_2","doi-asserted-by":"crossref","first-page":"26","DOI":"10.9781\/ijimai.2016.415","article-title":"Multilayer perceptron: Architecture optimization and training","volume":"4","author":"Ramchoun Hassan","year":"2016","unstructured":"Hassan Ramchoun, Mohammed Amine Janati Idrissi, Youssef Ghanou, and Mohamed Ettaouil. 2016. Multilayer perceptron: Architecture optimization and training. International Journal of Interactive Multimedia and Artificial Intelligence 4, 1 (2016), 26\u201330.","journal-title":"International Journal of Interactive Multimedia and Artificial Intelligence"},{"key":"e_1_3_2_69_2","unstructured":"Emanuele Rossi Ben Chamberlain Fabrizio Frasca Davide Eynard Federico Monti and Michael M. Bronstein. 2020. Temporal graph networks for deep learning on dynamic graphs. arXiv:2006.10637. Retrieved from https:\/\/arxiv.org\/abs\/2006.10637"},{"issue":"2","key":"e_1_3_2_70_2","first-page":"72","article-title":"A short proof of optimality for the MIN cache replacement algorithm","volume":"102","author":"Roy Benjamin Van","year":"2007","unstructured":"Benjamin Van Roy. 2007. A short proof of optimality for the MIN cache replacement algorithm. Information Process. Letter 102, 2-3 (2007), 72\u201373.","journal-title":"Information Process. Letter"},{"key":"e_1_3_2_71_2","unstructured":"Sebastian Ruder. 2016. An overview of gradient descent optimization algorithms. arXiv:1609.04747. Retrieved from https:\/\/arxiv.org\/abs\/1609.04747"},{"key":"e_1_3_2_72_2","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1145\/3336191.3371845","volume-title":"Proceedings of the 13th International Conference on Web Search and Data Mining","author":"Sankar Aravind","year":"2020","unstructured":"Aravind Sankar, Yanhong Wu, Liang Gou, Wei Zhang, and Hao Yang. 2020. DySAT: Deep neural representation learning on dynamic graphs via self-attention networks. In Proceedings of the 13th International Conference on Web Search and Data Mining. ACM, 519\u2013527."},{"key":"e_1_3_2_73_2","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1145\/564376.564421","volume-title":"Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Schein Andrew I.","year":"2002","unstructured":"Andrew I. Schein, Alexandrin Popescul, Lyle H. Ungar, and David M. Pennock. 2002. Methods and metrics for cold-start recommendations. In Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 253\u2013260."},{"key":"e_1_3_2_74_2","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1007\/978-3-030-04167-0_33","volume-title":"Proceedings of the Neural Information Processing: 25th International Conference, ICONIP 2018","volume":"11301","author":"Seo Youngjoo","year":"2018","unstructured":"Youngjoo Seo, Micha\u00ebl Defferrard, Pierre Vandergheynst, and Xavier Bresson. 2018. Structured sequence modeling with graph convolutional recurrent networks. In Proceedings of the Neural Information Processing: 25th International Conference, ICONIP 2018, Vol. 11301. Springer, 362\u2013373."},{"key":"e_1_3_2_75_2","doi-asserted-by":"crossref","first-page":"1171","DOI":"10.1145\/3299869.3319863","volume-title":"Proceedings of the 2019 International Conference on Management of Data","author":"Shang Zeyuan","year":"2019","unstructured":"Zeyuan Shang, Emanuel Zgraggen, Benedetto Buratti, Ferdinand Kossmann, Philipp Eichmann, Yeounoh Chung, Carsten Binnig, Eli Upfal, and Tim Kraska. 2019. Democratizing data science through interactive curation of ML pipelines. In Proceedings of the 2019 International Conference on Management of Data. ACM, 1171\u20131188."},{"key":"e_1_3_2_76_2","unstructured":"Zhihao Shi Xize Liang and Jie Wang. 2023. LMC: Fast training of GNNs via subgraph sampling with provable convergence. In The Eleventh International Conference on Learning Representations ICLR 2023 Kigali Rwanda May 1-5 2023 OpenReview.net. Retrieved from https:\/\/openreview.net\/forum?id=5VBBA91N6n"},{"key":"e_1_3_2_77_2","doi-asserted-by":"crossref","first-page":"79143","DOI":"10.1109\/ACCESS.2021.3082932","article-title":"Foundations and modeling of dynamic networks using dynamic graph neural networks: A survey","volume":"9","author":"Skarding Joakim","year":"2021","unstructured":"Joakim Skarding, Bogdan Gabrys, and Katarzyna Musial. 2021. Foundations and modeling of dynamic networks using dynamic graph neural networks: A survey. IEEE Access 9 (2021), 79143\u201379168.","journal-title":"IEEE Access"},{"key":"e_1_3_2_78_2","first-page":"535","volume-title":"2017 IEEE 33rd International Conference on Data Engineering","author":"Sparks Evan R.","year":"2017","unstructured":"Evan R. Sparks, Shivaram Venkataraman, Tomer Kaftan, Michael J. Franklin, and Benjamin Recht. 2017. KeystoneML: Optimizing pipelines for large-scale advanced analytics. In 2017 IEEE 33rd International Conference on Data Engineering. IEEE Computer Society, 535\u2013546."},{"key":"e_1_3_2_79_2","first-page":"495","volume-title":"15th USENIX Symposium on Operating Systems Design and Implementation","author":"Thorpe John","year":"2021","unstructured":"John Thorpe, Yifan Qiao, Jonathan Eyolfson, Shen Teng, Guanzhou Hu, Zhihao Jia, Jinliang Wei, Keval Vora, Ravi Netravali, Miryung Kim, and Guoqing Harry Xu. 2021. Dorylus: Affordable, scalable, and accurate GNN training with distributed CPU servers and serverless threads. In 15th USENIX Symposium on Operating Systems Design and Implementation. USENIX Association, 495\u2013514."},{"key":"e_1_3_2_80_2","volume-title":"Proceedings of the 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 Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_2_81_2","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.1145\/3183713.3196934","volume-title":"Proceedings of the 2018 International Conference on Management of Data","author":"Vartak Manasi","year":"2018","unstructured":"Manasi Vartak, Joana M. F. da Trindade, Samuel Madden, and Matei Zaharia. 2018. MISTIQUE: A system to store and query model intermediates for model diagnosis. In Proceedings of the 2018 International Conference on Management of Data. ACM, 1285\u20131300."},{"issue":"4","key":"e_1_3_2_82_2","first-page":"16","article-title":"MODELDB: Opportunities and challenges in managing machine learning models","volume":"41","author":"Vartak Manasi","year":"2018","unstructured":"Manasi Vartak and Samuel Madden. 2018. MODELDB: Opportunities and challenges in managing machine learning models. IEEE Data Engineering Bulletin 41, 4 (2018), 16\u201325.","journal-title":"IEEE Data Engineering Bulletin"},{"key":"e_1_3_2_83_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Velickovic Petar","year":"2018","unstructured":"Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Li\u00f2, and Yoshua Bengio. 2018. Graph attention networks. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_2_84_2","first-page":"3839","volume-title":"Proceedings of the 32nd International Conference on Neural Information Processing Systems","author":"Virmaux Aladin","year":"2018","unstructured":"Aladin Virmaux and Kevin Scaman. 2018. Lipschitz regularity of deep neural networks: Analysis and efficient estimation. In Proceedings of the 32nd International Conference on Neural Information Processing Systems. 3839\u20133848."},{"key":"e_1_3_2_85_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Wan Cheng","year":"2022","unstructured":"Cheng Wan, Youjie Li, Cameron R. Wolfe, Anastasios Kyrillidis, Nam Sung Kim, and Yingyan Lin. 2022. PipeGCN: Efficient full-graph training of graph convolutional networks with pipelined feature communication. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_2_86_2","doi-asserted-by":"crossref","first-page":"2628","DOI":"10.1145\/3448016.3457564","volume-title":"Proceedings of the 2021 International Conference on Management of Data","author":"Wang Xuhong","year":"2021","unstructured":"Xuhong Wang, Ding Lyu, Mengjian Li, Yang Xia, Qi Yang, Xinwen Wang, Xinguang Wang, Ping Cui, Yupu Yang, Bowen Sun, and Zhenyu Guo. 2021. APAN: Asynchronous propagation attention network for real-time temporal graph embedding. In Proceedings of the 2021 International Conference on Management of Data. ACM, 2628\u20132638."},{"key":"e_1_3_2_87_2","volume-title":"Proceedings of the International Conference on Learning Representations","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 Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_2_88_2","doi-asserted-by":"crossref","unstructured":"Zhihao Wen and Yuan Fang. 2022. TREND: TempoRal event and node dynamics for graph representation learning. In Proceedings of the ACM Web Conference 2022. ACM 1159\u20131169.","DOI":"10.1145\/3485447.3512164"},{"key":"e_1_3_2_89_2","first-page":"9:1\u20139:4","volume-title":"Proceedings of the 2nd Workshop on Data Management for End-To-End Machine Learning, DEEM@SIGMOD","author":"Xin Doris","year":"2018","unstructured":"Doris Xin, Litian Ma, Jialin Liu, Stephen Macke, Shuchen Song, and Aditya G. Parameswaran. 2018. Accelerating human-in-the-loop machine learning: Challenges and opportunities. In Proceedings of the 2nd Workshop on Data Management for End-To-End Machine Learning, DEEM@SIGMOD. ACM, 9:1\u20139:4."},{"issue":"12","key":"e_1_3_2_90_2","first-page":"1958","article-title":"Helix: Accelerating human-in-the-loop machine learning","volume":"11","author":"Xin Doris","year":"2018","unstructured":"Doris Xin, Litian Ma, Jialin Liu, Stephen Macke, Shuchen Song, and Aditya G. Parameswaran. 2018. Helix: Accelerating human-in-the-loop machine learning. PVLDB 11, 12 (2018), 1958\u20131961.","journal-title":"PVLDB"},{"issue":"4","key":"e_1_3_2_91_2","first-page":"446","article-title":"Helix: Holistic optimization for accelerating iterative machine learning","volume":"12","author":"Xin Doris","year":"2018","unstructured":"Doris Xin, Stephen Macke, Litian Ma, Jialin Liu, Shuchen Song, and Aditya G. Parameswaran. 2018. Helix: Holistic optimization for accelerating iterative machine learning. PVLDB 12, 4 (2018), 446\u2013460.","journal-title":"PVLDB"},{"key":"e_1_3_2_92_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Xu Da","year":"2020","unstructured":"Da Xu, Chuanwei Ruan, Evren K\u00f6rpeoglu, Sushant Kumar, and Kannan Achan. 2020. Inductive representation learning on temporal graphs. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_2_93_2","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.neucom.2021.03.138","article-title":"Dynamic network embedding survey","volume":"472","author":"Xue Guotong","year":"2022","unstructured":"Guotong Xue, Ming Zhong, Jianxin Li, Jia Chen, Chengshuai Zhai, and Ruochen Kong. 2022. Dynamic network embedding survey. Neurocomputing 472 (2022), 212\u2013223.","journal-title":"Neurocomputing"},{"key":"e_1_3_2_94_2","first-page":"4573","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Yang Han","year":"2021","unstructured":"Han Yang, Kaili Ma, and James Cheng. 2021. Rethinking graph regularization for graph neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence. AAAI Press, 4573\u20134581."},{"key":"e_1_3_2_95_2","first-page":"2358","volume-title":"Proceedings of the Knowledge Discovery and Data Mining","author":"You Jiaxuan","year":"2022","unstructured":"Jiaxuan You, Tianyu Du, and Jure Leskovec. 2022. ROLAND: Graph learning framework for dynamic graphs. In Proceedings of the Knowledge Discovery and Data Mining. ACM, 2358\u20132366."},{"key":"e_1_3_2_96_2","series-title":"Proceedings of Machine Learning Research","first-page":"25684","volume-title":"Proceedings of the International Conference on Machine Learning","volume":"162","author":"Yu Haiyang","year":"2022","unstructured":"Haiyang Yu, Limei Wang, Bokun Wang, Meng Liu, Tianbao Yang, and Shuiwang Ji. 2022. GraphFM: Improving large-scale GNN training via feature momentum. In Proceedings of the International Conference on Machine Learning(Proceedings of Machine Learning Research, Vol. 162). PMLR, 25684\u201325701."},{"key":"e_1_3_2_97_2","first-page":"2672","volume-title":"Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","author":"Yu Wenchao","year":"2018","unstructured":"Wenchao Yu, Wei Cheng, Charu C. Aggarwal, Kai Zhang, Haifeng Chen, and Wei Wang. 2018. NetWalk: A flexible deep embedding approach for anomaly detection in dynamic networks. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 2672\u20132681."},{"key":"e_1_3_2_98_2","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Zeng Hanqing","year":"2020","unstructured":"Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, and Viktor K. Prasanna. 2020. GraphSAINT: Graph sampling based inductive learning method. In Proceedings of the International Conference on Machine Learning."},{"issue":"1","key":"e_1_3_2_99_2","doi-asserted-by":"crossref","first-page":"2:1\u20132:32","DOI":"10.1145\/2877204","article-title":"Materialization optimizations for feature selection workloads","volume":"41","author":"Zhang Ce","year":"2016","unstructured":"Ce Zhang, Arun Kumar, and Christopher R\u00e9. 2016. Materialization optimizations for feature selection workloads. ACM Transactions on Database Systems 41, 1 (2016), 2:1\u20132:32.","journal-title":"ACM Transactions on Database Systems"},{"key":"e_1_3_2_100_2","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Zhang Jingzhao","year":"2020","unstructured":"Jingzhao Zhang, Tianxing He, Suvrit Sra, and Ali Jadbabaie. 2020. Why gradient clipping accelerates training: A theoretical justification for adaptivity. In Proceedings of the International Conference on Machine Learning. 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