{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T23:03:10Z","timestamp":1780614190920,"version":"3.54.1"},"reference-count":76,"publisher":"Association for Computing Machinery (ACM)","issue":"11","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2023,7]]},"abstract":"<jats:p>Recent advances in Graph Neural Networks (GNNs) have changed the landscape of modern graph analytics. The complexity of GNN training and the scalability challenges have also sparked interest from the systems community, with efforts to build systems that provide higher efficiency and schemes to reduce costs. However, we observe that many such systems basically \"reinvent the wheel\" of much work done in the database world on scalable graph analytics engines. Further, they often tightly couple the scalability treatments of graph data processing with that of GNN training, resulting in entangled complex problems and systems that often do not scale well on one of those axes.<\/jats:p>\n          <jats:p>\n            In this paper, we ask a fundamental question: How far can we push existing systems for scalable graph analytics and deep learning (DL) instead of building custom GNN systems? Are compromises inevitable on scalability and\/or runtimes? We propose Lotan, the first scalable and optimized data system for full-batch GNN training with\n            <jats:italic>decoupled scaling<\/jats:italic>\n            that bridges the hitherto siloed worlds of graph analytics systems and DL systems. Lotan offers a series of technical innovations, including re-imagining GNN training as query plan-like dataflows, execution plan rewriting, optimized data movement between systems, a GNN-centric graph partitioning scheme, and the first known GNN model batching scheme. We prototyped Lotan on top of GraphX and PyTorch. An empirical evaluation using several real-world benchmark GNN workloads reveals a promising nuanced picture: Lotan significantly surpasses the scalability of state-of-the-art custom GNN systems, while often matching or being only slightly behind on time-to-accuracy metrics in some cases. We also show the impact of our system optimizations. Overall, our work shows that the GNN world can indeed benefit from building on top of scalable graph analytics engines. Lotan's new level of scalability can also empower new ML-oriented research on ever-larger graphs and GNNs.\n          <\/jats:p>","DOI":"10.14778\/3611479.3611483","type":"journal-article","created":{"date-parts":[[2023,8,25]],"date-time":"2023-08-25T02:08:08Z","timestamp":1692929288000},"page":"2728-2741","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Lotan: Bridging the Gap between GNNs and Scalable Graph Analytics Engines"],"prefix":"10.14778","volume":"16","author":[{"given":"Yuhao","family":"Zhang","sequence":"first","affiliation":[{"name":"University of California, San Diego"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arun","family":"Kumar","sequence":"additional","affiliation":[{"name":"University of California, San Diego"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,8,24]]},"reference":[{"key":"e_1_2_1_1_1","first-page":"265","volume-title":"OSDI","author":"Abadi M.","year":"2016","unstructured":"M. Abadi , P. Barham , J. Chen , Z. Chen , A. Davis , J. Dean , M. Devin , S. Ghemawat , G. Irving , M. Isard , M. Kudlur , J. Levenberg , R. Monga , S. Moore , D. G. Murray , B. Steiner , P. A. Tucker , V. Vasudevan , P. Warden , M. Wicke , Y. Yu , and X. Zheng . Tensorflow: A system for large-scale machine learning . In OSDI , pages 265 -- 283 . USENIX Association , 2016 . M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. A. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, and X. Zheng. Tensorflow: A system for large-scale machine learning. In OSDI, pages 265--283. USENIX Association, 2016."},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bdr.2017.05.003"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2021.3065737"},{"key":"e_1_2_1_4_1","volume-title":"Demystifying graph databases: Analysis and taxonomy of data organization, system designs, and graph queries. CoRR, abs\/1910.09017","author":"Besta M.","year":"2019","unstructured":"M. Besta , E. Peter , R. Gerstenberger , M. Fischer , M. Podstawski , C. Barthels , G. Alonso , and T. Hoefler . Demystifying graph databases: Analysis and taxonomy of data organization, system designs, and graph queries. CoRR, abs\/1910.09017 , 2019 . M. Besta, E. Peter, R. Gerstenberger, M. Fischer, M. Podstawski, C. Barthels, G. Alonso, and T. Hoefler. Demystifying graph databases: Analysis and taxonomy of data organization, system designs, and graph queries. CoRR, abs\/1910.09017, 2019."},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-49487-6_4"},{"key":"e_1_2_1_6_1","volume-title":"ICLR (Poster). OpenReview.net","author":"Chen J.","year":"2018","unstructured":"J. Chen , T. Ma , and C. Xiao . Fastgcn: Fast learning with graph convolutional networks via importance sampling . In ICLR (Poster). OpenReview.net , 2018 . J. Chen, T. Ma, and C. Xiao. Fastgcn: Fast learning with graph convolutional networks via importance sampling. In ICLR (Poster). OpenReview.net, 2018."},{"key":"e_1_2_1_7_1","series-title":"Proceedings of Machine Learning Research","first-page":"941","volume-title":"ICML","author":"Chen J.","year":"2018","unstructured":"J. Chen , J. Zhu , and L. Song . Stochastic training of graph convolutional networks with variance reduction . In ICML , volume 80 of Proceedings of Machine Learning Research , pages 941 -- 949 . PMLR , 2018 . J. Chen, J. Zhu, and L. Song. Stochastic training of graph convolutional networks with variance reduction. In ICML, volume 80 of Proceedings of Machine Learning Research, pages 941--949. PMLR, 2018."},{"key":"e_1_2_1_8_1","series-title":"Proceedings of Machine Learning Research","first-page":"1725","volume-title":"ICML","author":"Chen M.","year":"2020","unstructured":"M. Chen , Z. Wei , Z. Huang , B. Ding , and Y. Li . Simple and deep graph convolutional networks . In ICML , volume 119 of Proceedings of Machine Learning Research , pages 1725 -- 1735 . PMLR , 2020 . M. Chen, Z. Wei, Z. Huang, B. Ding, and Y. Li. Simple and deep graph convolutional networks. In ICML, volume 119 of Proceedings of Machine Learning Research, pages 1725--1735. PMLR, 2020."},{"key":"e_1_2_1_9_1","volume-title":"Tigergraph: A native MPP graph database. CoRR, abs\/1901.08248","author":"Deutsch A.","year":"2019","unstructured":"A. Deutsch , Y. Xu , M. Wu , and V. E. Lee . Tigergraph: A native MPP graph database. CoRR, abs\/1901.08248 , 2019 . A. Deutsch, Y. Xu, M. Wu, and V. E. Lee. Tigergraph: A native MPP graph database. CoRR, abs\/1901.08248, 2019."},{"key":"e_1_2_1_10_1","first-page":"4171","volume-title":"NAACL-HLT (1)","author":"Devlin J.","year":"2019","unstructured":"J. Devlin , M. Chang , K. Lee , and K. Toutanova . BERT: pre-training of deep bidirectional transformers for language understanding . In NAACL-HLT (1) , pages 4171 -- 4186 . Association for Computational Linguistics , 2019 . J. Devlin, M. Chang, K. Lee, and K. Toutanova. BERT: pre-training of deep bidirectional transformers for language understanding. In NAACL-HLT (1), pages 4171--4186. Association for Computational Linguistics, 2019."},{"key":"e_1_2_1_11_1","volume-title":"CIDR","author":"Feng X.","year":"2023","unstructured":"X. Feng , G. Jin , Z. Chen , C. Liu , and S. Saliho\u011flu . K\u00f9zu graph database management system . In CIDR , 2023 . X. Feng, G. Jin, Z. Chen, C. Liu, and S. Saliho\u011flu. K\u00f9zu graph database management system. In CIDR, 2023."},{"key":"e_1_2_1_12_1","volume-title":"Fast graph representation learning with pytorch geometric. CoRR, abs\/1903.02428","author":"Fey M.","year":"2019","unstructured":"M. Fey and J. E. Lenssen . Fast graph representation learning with pytorch geometric. CoRR, abs\/1903.02428 , 2019 . M. Fey and J. E. Lenssen. Fast graph representation learning with pytorch geometric. CoRR, abs\/1903.02428, 2019."},{"key":"e_1_2_1_13_1","first-page":"551","volume-title":"OSDI","author":"Gandhi S.","year":"2021","unstructured":"S. Gandhi and A. P. Iyer . P3: distributed deep graph learning at scale . In OSDI , pages 551 -- 568 . USENIX Association , 2021 . S. Gandhi and A. P. Iyer. P3: distributed deep graph learning at scale. In OSDI, pages 551--568. USENIX Association, 2021."},{"key":"e_1_2_1_14_1","series-title":"Proceedings of Machine Learning Research","first-page":"1263","volume-title":"ICML","author":"Gilmer J.","year":"2017","unstructured":"J. Gilmer , S. S. Schoenholz , P. F. Riley , O. Vinyals , and G. E. Dahl . Neural message passing for quantum chemistry . In ICML , volume 70 of Proceedings of Machine Learning Research , pages 1263 -- 1272 . PMLR , 2017 . J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals, and G. E. Dahl. Neural message passing for quantum chemistry. In ICML, volume 70 of Proceedings of Machine Learning Research, pages 1263--1272. PMLR, 2017."},{"key":"e_1_2_1_15_1","unstructured":"Giraph. Apache Giraph Accessed Nov 2 2022. https:\/\/giraph.apache.org\/.  Giraph. Apache Giraph Accessed Nov 2 2022. https:\/\/giraph.apache.org\/."},{"key":"e_1_2_1_16_1","first-page":"17","volume-title":"OSDI","author":"Gonzalez J. E.","year":"2012","unstructured":"J. E. Gonzalez , Y. Low , H. Gu , D. Bickson , and C. Guestrin . Powergraph: Distributed graph-parallel computation on natural graphs . In OSDI , pages 17 -- 30 . USENIX Association , 2012 . J. E. Gonzalez, Y. Low, H. Gu, D. Bickson, and C. Guestrin. Powergraph: Distributed graph-parallel computation on natural graphs. In OSDI, pages 17--30. USENIX Association, 2012."},{"key":"e_1_2_1_17_1","first-page":"599","volume-title":"OSDI","author":"Gonzalez J. E.","year":"2014","unstructured":"J. E. Gonzalez , R. S. Xin , A. Dave , D. Crankshaw , M. J. Franklin , and I. Stoica . Graphx: Graph processing in a distributed dataflow framework . In OSDI , pages 599 -- 613 . USENIX Association , 2014 . J. E. Gonzalez, R. S. Xin, A. Dave, D. Crankshaw, M. J. Franklin, and I. Stoica. Graphx: Graph processing in a distributed dataflow framework. In OSDI, pages 599--613. USENIX Association, 2014."},{"key":"e_1_2_1_18_1","first-page":"1024","volume-title":"NIPS","author":"Hamilton W. L.","year":"2017","unstructured":"W. L. Hamilton , Z. Ying , and J. Leskovec . Inductive representation learning on large graphs . In NIPS , pages 1024 -- 1034 , 2017 . W. L. Hamilton, Z. Ying, and J. Leskovec. Inductive representation learning on large graphs. In NIPS, pages 1024--1034, 2017."},{"key":"e_1_2_1_19_1","first-page":"507","volume-title":"Proceedings of the 25th International Conference on World Wide Web, WWW 2016","author":"He R.","year":"2016","unstructured":"R. He and J. J. McAuley . Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In J. Bourdeau, J. Hendler, R. Nkambou, I. Horrocks, and B. Y. Zhao, editors , Proceedings of the 25th International Conference on World Wide Web, WWW 2016 , Montreal, Canada, April 11 -- 15 , 2016 , pages 507 -- 517 . ACM, 2016. R. He and J. J. McAuley. Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In J. Bourdeau, J. Hendler, R. Nkambou, I. Horrocks, and B. Y. Zhao, editors, Proceedings of the 25th International Conference on World Wide Web, WWW 2016, Montreal, Canada, April 11 -- 15, 2016, pages 507--517. ACM, 2016."},{"key":"e_1_2_1_20_1","volume-title":"MLSys GNNSys Workshop. mlsys.org","author":"Hoang L.","year":"2021","unstructured":"L. Hoang , X. Chen , H. Lee , R. Dathathri , G. Gill , and K. Pingali . Efficient distribution for deep learning on large graphs . In MLSys GNNSys Workshop. mlsys.org , 2021 . L. Hoang, X. Chen, H. Lee, R. Dathathri, G. Gill, and K. Pingali. Efficient distribution for deep learning on large graphs. In MLSys GNNSys Workshop. mlsys.org, 2021."},{"key":"e_1_2_1_21_1","volume-title":"NeurIPS","author":"Hu W.","year":"2020","unstructured":"W. Hu , M. Fey , M. Zitnik , Y. Dong , H. Ren , B. Liu , M. Catasta , and J. Leskovec . Open graph benchmark: Datasets for machine learning on graphs . In NeurIPS , 2020 . W. Hu, M. Fey, M. Zitnik, Y. Dong, H. Ren, B. Liu, M. Catasta, and J. Leskovec. Open graph benchmark: Datasets for machine learning on graphs. In NeurIPS, 2020."},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467256"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.573"},{"key":"e_1_2_1_24_1","volume-title":"Proceedings of Machine Learning and Systems 2020","author":"Jia Z.","year":"2020","unstructured":"Z. Jia , S. Lin , M. Gao , M. Zaharia , and A. Aiken . Improving the accuracy, scalability, and performance of graph neural networks with roc. In I. S. Dhillon, D. S. Papailiopoulos, and V. Sze, editors , Proceedings of Machine Learning and Systems 2020 , MLSys 2020 , Austin, TX, USA, March 2--4 , 2020. mlsys.org, 2020. Z. Jia, S. Lin, M. Gao, M. Zaharia, and A. Aiken. Improving the accuracy, scalability, and performance of graph neural networks with roc. In I. S. Dhillon, D. S. Papailiopoulos, and V. Sze, editors, Proceedings of Machine Learning and Systems 2020, MLSys 2020, Austin, TX, USA, March 2--4, 2020. mlsys.org, 2020."},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE48307.2020.00137"},{"key":"e_1_2_1_26_1","volume-title":"ICLR (Poster). OpenReview.net","author":"Kipf T. N.","year":"2017","unstructured":"T. N. Kipf and M. Welling . Semi-supervised classification with graph convolutional networks . In ICLR (Poster). OpenReview.net , 2017 . T. N. Kipf and M. Welling. Semi-supervised classification with graph convolutional networks. In ICLR (Poster). OpenReview.net, 2017."},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3183713.3196915"},{"key":"e_1_2_1_28_1","volume-title":"CIDR. www.cidrdb.org","author":"Kumar A.","year":"2021","unstructured":"A. Kumar , S. Nakandala , Y. Zhang , S. Li , A. Gemawat , and K. Nagrecha . Cerebro: A Layered Data Platform for Scalable Deep Learning . In CIDR. www.cidrdb.org , 2021 . A. Kumar, S. Nakandala, Y. Zhang, S. Li, A. Gemawat, and K. Nagrecha. Cerebro: A Layered Data Platform for Scalable Deep Learning. In CIDR. www.cidrdb.org, 2021."},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10619-014-7140-3"},{"key":"e_1_2_1_30_1","volume-title":"MLSys. mlsys.org","author":"Lerer A.","year":"2019","unstructured":"A. Lerer , L. Wu , J. Shen , T. Lacroix , L. Wehrstedt , A. Bose , and A. Peysakhovich . Pytorch-biggraph: A large scale graph embedding system . In MLSys. mlsys.org , 2019 . A. Lerer, L. Wu, J. Shen, T. Lacroix, L. Wehrstedt, A. Bose, and A. Peysakhovich. Pytorch-biggraph: A large scale graph embedding system. In MLSys. mlsys.org, 2019."},{"key":"e_1_2_1_31_1","series-title":"Proceedings of Machine Learning Research","first-page":"6437","volume-title":"Proceedings of the 38th International Conference on Machine Learning, ICML","author":"Li G.","year":"2021","unstructured":"G. Li , M. M\u00fcller , B. Ghanem , and V. Koltun . Training graph neural networks with 1000 layers . In M. Meila and T. Zhang, editors, Proceedings of the 38th International Conference on Machine Learning, ICML 2021 , 18--24 July 2021, Virtual Event, volume 139 of Proceedings of Machine Learning Research , pages 6437 -- 6449 . PMLR , 2021. G. Li, M. M\u00fcller, B. Ghanem, and V. Koltun. Training graph neural networks with 1000 layers. In M. Meila and T. Zhang, editors, Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18--24 July 2021, Virtual Event, volume 139 of Proceedings of Machine Learning Research, pages 6437--6449. PMLR, 2021."},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.5555\/2685048.2685095"},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.14778\/3476249.3476284"},{"key":"e_1_2_1_34_1","volume-title":"Multiphysical graph neural network (MP-GNN) for COVID-19 drug design. Briefings Bioinform., 23(4)","author":"Li X.","year":"2022","unstructured":"X. Li , X. Liu , L. Lu , X. Hua , Y. Chi , and K. Xia . Multiphysical graph neural network (MP-GNN) for COVID-19 drug design. Briefings Bioinform., 23(4) , 2022 . X. Li, X. Liu, L. Lu, X. Hua, Y. Chi, and K. Xia. Multiphysical graph neural network (MP-GNN) for COVID-19 drug design. Briefings Bioinform., 23(4), 2022."},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3419111.3421281"},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.14778\/3415478.3415482"},{"key":"e_1_2_1_37_1","first-page":"18762","volume-title":"NeurIPS","author":"Liu J.","year":"2021","unstructured":"J. Liu , K. Kawaguchi , B. Hooi , Y. Wang , and X. Xiao . EIGNN: efficient infinite-depth graph neural networks . In NeurIPS , pages 18762 -- 18773 , 2021 . J. Liu, K. Kawaguchi, B. Hooi, Y. Wang, and X. Xiao. EIGNN: efficient infinite-depth graph neural networks. In NeurIPS, pages 18762--18773, 2021."},{"key":"e_1_2_1_38_1","first-page":"340","volume-title":"UAI","author":"Low Y.","year":"2010","unstructured":"Y. Low , J. Gonzalez , A. Kyrola , D. Bickson , C. Guestrin , and J. M. Hellerstein . Graphlab: A new framework for parallel machine learning . In UAI , pages 340 -- 349 . AUAI Press , 2010 . Y. Low, J. Gonzalez, A. Kyrola, D. Bickson, C. Guestrin, and J. M. Hellerstein. Graphlab: A new framework for parallel machine learning. In UAI, pages 340--349. AUAI Press, 2010."},{"key":"e_1_2_1_39_1","first-page":"443","volume-title":"USENIX Annual Technical Conference","author":"Ma L.","year":"2019","unstructured":"L. Ma , Z. Yang , Y. Miao , J. Xue , M. Wu , L. Zhou , and Y. Dai . Neugraph: Parallel deep neural network computation on large graphs . In USENIX Annual Technical Conference , pages 443 -- 458 . USENIX Association , 2019 . L. Ma, Z. Yang, Y. Miao, J. Xue, M. Wu, L. Zhou, and Y. Dai. Neugraph: Parallel deep neural network computation on large graphs. In USENIX Annual Technical Conference, pages 443--458. USENIX Association, 2019."},{"key":"e_1_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/1807167.1807184"},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE53745.2022.00157"},{"key":"e_1_2_1_42_1","first-page":"533","volume-title":"OSDI","author":"Mohoney J.","year":"2021","unstructured":"J. Mohoney , R. Waleffe , H. Xu , T. Rekatsinas , and S. Venkataraman . Marius: Learning massive graph embeddings on a single machine . In OSDI , pages 533 -- 549 . USENIX Association , 2021 . J. Mohoney, R. Waleffe, H. Xu, T. Rekatsinas, and S. Venkataraman. Marius: Learning massive graph embeddings on a single machine. In OSDI, pages 533--549. USENIX Association, 2021."},{"key":"e_1_2_1_43_1","volume-title":"Saturn: An Optimized Data System for Multi-Large-Model Deep Learning Workloads. https:\/\/adalabucsd.github.io\/papers\/TR_2023_Saturn.pdf","author":"Nagrecha K.","year":"2023","unstructured":"K. Nagrecha and A. Kumar . Saturn: An Optimized Data System for Multi-Large-Model Deep Learning Workloads. https:\/\/adalabucsd.github.io\/papers\/TR_2023_Saturn.pdf , 2023 . [Tech report]. K. Nagrecha and A. Kumar. Saturn: An Optimized Data System for Multi-Large-Model Deep Learning Workloads. https:\/\/adalabucsd.github.io\/papers\/TR_2023_Saturn.pdf, 2023. [Tech report]."},{"key":"e_1_2_1_44_1","first-page":"506","volume-title":"SIGMOD '22: International Conference on Management of Data","author":"Nakandala S.","year":"2022","unstructured":"S. Nakandala and A. Kumar . Nautilus: An optimized system for deep transfer learning over evolving training datasets. In Z. G. Ives, A. Bonifati, and A. E. Abbadi, editors , SIGMOD '22: International Conference on Management of Data , Philadelphia, PA, USA, June 12 -- 17 , 2022 , pages 506 -- 520 . ACM, 2022. S. Nakandala and A. Kumar. Nautilus: An optimized system for deep transfer learning over evolving training datasets. In Z. G. Ives, A. Bonifati, and A. E. Abbadi, editors, SIGMOD '22: International Conference on Management of Data, Philadelphia, PA, USA, June 12 -- 17, 2022, pages 506--520. ACM, 2022."},{"key":"e_1_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/3329486.3329496"},{"key":"e_1_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.14778\/3407790.3407816"},{"key":"e_1_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/3341301.3359646"},{"key":"e_1_2_1_48_1","volume-title":"NeurIPS Workshop on Systems for Machine Learning","author":"Narayanan D.","year":"2018","unstructured":"D. Narayanan , K. Santhanam , A. Phanishayee , and M. Zaharia . Accelerating deep learning workloads through efficient multi-model execution . In NeurIPS Workshop on Systems for Machine Learning , December 2018 . D. Narayanan, K. Santhanam, A. Phanishayee, and M. Zaharia. Accelerating deep learning workloads through efficient multi-model execution. In NeurIPS Workshop on Systems for Machine Learning, December 2018."},{"key":"e_1_2_1_49_1","first-page":"o4j","year":"2021","unstructured":"Neo4j. Neo4j , Accessed October 12, 2021 . https:\/\/ne o4j .com\/. Neo4j. Neo4j, Accessed October 12, 2021. https:\/\/neo4j.com\/.","journal-title":"Accessed"},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.14778\/3538598.3538614"},{"key":"e_1_2_1_51_1","volume-title":"Improving language understanding by generative pre-training","author":"Radford A.","year":"2018","unstructured":"A. Radford and K. Narasimhan . Improving language understanding by generative pre-training . 2018 . A. Radford and K. Narasimhan. Improving language understanding by generative pre-training. 2018."},{"key":"e_1_2_1_52_1","volume-title":"Introducing Cloudlab: Scientific Infrastructure for Advancing Cloud Architectures and Applications","author":"Ricci R.","year":"2014","unstructured":"R. Ricci , E. Eide , and CloudLabTeam. Introducing Cloudlab: Scientific Infrastructure for Advancing Cloud Architectures and Applications . ; login:: the magazine of USENIX & SAGE , 39(6):36--38, 2014 . R. Ricci, E. Eide, and CloudLabTeam. Introducing Cloudlab: Scientific Infrastructure for Advancing Cloud Architectures and Applications. ; login:: the magazine of USENIX & SAGE, 39(6):36--38, 2014."},{"key":"e_1_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1145\/2517349.2522740"},{"key":"e_1_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1145\/3434642"},{"key":"e_1_2_1_55_1","volume-title":"Horovod: Fast and Easy Distributed Deep Learning in TF. arXiv preprint arXiv:1802.05799","author":"Sergeev A.","year":"2018","unstructured":"A. Sergeev and M. D. Balso . Horovod: Fast and Easy Distributed Deep Learning in TF. arXiv preprint arXiv:1802.05799 , 2018 . A. Sergeev and M. D. Balso. Horovod: Fast and Easy Distributed Deep Learning in TF. arXiv preprint arXiv:1802.05799, 2018."},{"key":"e_1_2_1_56_1","series-title":"Lecture Notes in Computer Science","first-page":"52","volume-title":"ECML\/PKDD (5)","author":"Sun J.","year":"2020","unstructured":"J. Sun , M. Yue , Z. Lin , X. Yang , L. Nocera , G. Kahn , and C. Shahabi . Crime-forecaster: Crime prediction by exploiting the geographical neighborhoods' spatiotemporal dependencies . In ECML\/PKDD (5) , volume 12461 of Lecture Notes in Computer Science , pages 52 -- 67 . Springer , 2020 . J. Sun, M. Yue, Z. Lin, X. Yang, L. Nocera, G. Kahn, and C. Shahabi. Crime-forecaster: Crime prediction by exploiting the geographical neighborhoods' spatiotemporal dependencies. In ECML\/PKDD (5), volume 12461 of Lecture Notes in Computer Science, pages 52--67. Springer, 2020."},{"key":"e_1_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1145\/2815400.2815410"},{"key":"e_1_2_1_58_1","first-page":"495","volume-title":"OSDI","author":"Thorpe J.","year":"2021","unstructured":"J. Thorpe , Y. Qiao , J. Eyolfson , S. Teng , G. Hu , Z. Jia , J. Wei , K. Vora , R. Netravali , M. Kim , and G. H. Xu . Dorylus: Affordable, scalable, and accurate GNN training with distributed CPU servers and serverless threads . In OSDI , pages 495 -- 514 . USENIX Association , 2021 . J. Thorpe, Y. Qiao, J. Eyolfson, S. Teng, G. Hu, Z. Jia, J. Wei, K. Vora, R. Netravali, M. Kim, and G. H. Xu. Dorylus: Affordable, scalable, and accurate GNN training with distributed CPU servers and serverless threads. In OSDI, pages 495--514. USENIX Association, 2021."},{"key":"e_1_2_1_59_1","volume-title":"abs\/2211.13170","author":"Tian Y.","year":"2022","unstructured":"Y. Tian . The world of graph databases from an industry perspective. CoRR , abs\/2211.13170 , 2022 . Y. Tian. The world of graph databases from an industry perspective. CoRR, abs\/2211.13170, 2022."},{"key":"e_1_2_1_60_1","volume-title":"Mariusgnn: Resource-efficient out-of-core training of graph neural networks","author":"Waleffe R.","year":"2022","unstructured":"R. Waleffe , J. Mohoney , T. Rekatsinas , and S. Venkataraman . Mariusgnn: Resource-efficient out-of-core training of graph neural networks , 2022 . R. Waleffe, J. Mohoney, T. Rekatsinas, and S. Venkataraman. Mariusgnn: Resource-efficient out-of-core training of graph neural networks, 2022."},{"key":"e_1_2_1_61_1","volume-title":"ICLR. OpenReview.net","author":"Wan C.","year":"2022","unstructured":"C. Wan , Y. Li , C. R. Wolfe , A. Kyrillidis , N. S. Kim , and Y. Lin . Pipegcn: Efficient full-graph training of graph convolutional networks with pipelined feature communication . In ICLR. OpenReview.net , 2022 . C. Wan, Y. Li, C. R. Wolfe, A. Kyrillidis, N. S. Kim, and Y. Lin. Pipegcn: Efficient full-graph training of graph convolutional networks with pipelined feature communication. In ICLR. OpenReview.net, 2022."},{"key":"e_1_2_1_62_1","volume-title":"Deep graph library: Towards efficient and scalable deep learning on graphs. CoRR, abs\/1909.01315","author":"Wang M.","year":"2019","unstructured":"M. Wang , L. Yu , D. Zheng , Q. Gan , Y. Gai , Z. Ye , M. Li , J. Zhou , Q. Huang , C. Ma , Z. Huang , Q. Guo , H. Zhang , H. Lin , J. Zhao , J. Li , A. J. Smola , and Z. Zhang . Deep graph library: Towards efficient and scalable deep learning on graphs. CoRR, abs\/1909.01315 , 2019 . M. Wang, L. Yu, D. Zheng, Q. Gan, Y. Gai, Z. Ye, M. Li, J. Zhou, Q. Huang, C. Ma, Z. Huang, Q. Guo, H. Zhang, H. Lin, J. Zhao, J. Li, A. J. Smola, and Z. Zhang. Deep graph library: Towards efficient and scalable deep learning on graphs. CoRR, abs\/1909.01315, 2019."},{"key":"e_1_2_1_63_1","series-title":"Proceedings of Machine Learning Research","first-page":"6861","volume-title":"ICML","author":"Wu F.","year":"2019","unstructured":"F. Wu , A. H. S. Jr., T. Zhang , C. Fifty , T. Yu , and K. Q. Weinberger . Simplifying graph convolutional networks . In ICML , volume 97 of Proceedings of Machine Learning Research , pages 6861 -- 6871 . PMLR , 2019 . F. Wu, A. H. S. Jr., T. Zhang, C. Fifty, T. Yu, and K. Q. Weinberger. Simplifying graph convolutional networks. In ICML, volume 97 of Proceedings of Machine Learning Research, pages 6861--6871. PMLR, 2019."},{"key":"e_1_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.1145\/3535101"},{"key":"e_1_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.109124"},{"key":"e_1_2_1_66_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"e_1_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.1109\/BigComp54360.2022.00033"},{"key":"e_1_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.14778\/3476311.3476324"},{"key":"e_1_2_1_69_1","volume-title":"How powerful are graph neural networks? In ICLR. OpenReview.net","author":"Xu K.","year":"2019","unstructured":"K. Xu , W. Hu , J. Leskovec , and S. Jegelka . How powerful are graph neural networks? In ICLR. OpenReview.net , 2019 . K. Xu, W. Hu, J. Leskovec, and S. Jegelka. How powerful are graph neural networks? In ICLR. OpenReview.net, 2019."},{"key":"e_1_2_1_70_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219890"},{"key":"e_1_2_1_71_1","doi-asserted-by":"publisher","DOI":"10.1145\/3448016.3457325"},{"key":"e_1_2_1_72_1","volume-title":"Lotan: Bridging the Gap between GNNs and Scalable Graph Analytics Engines. https:\/\/adalabucsd.github.io\/papers\/TR_2023_Lotan.pdf","author":"Zhang Y.","year":"2023","unstructured":"Y. Zhang and A. Kumar . Lotan: Bridging the Gap between GNNs and Scalable Graph Analytics Engines. https:\/\/adalabucsd.github.io\/papers\/TR_2023_Lotan.pdf , 2023 . [Tech report]. Y. Zhang and A. Kumar. Lotan: Bridging the Gap between GNNs and Scalable Graph Analytics Engines. https:\/\/adalabucsd.github.io\/papers\/TR_2023_Lotan.pdf, 2023. [Tech report]."},{"key":"e_1_2_1_73_1","doi-asserted-by":"publisher","DOI":"10.14778\/3467861.3467867"},{"key":"e_1_2_1_74_1","volume-title":"The Eleventh International Conference on Learning Representations","author":"Zhao J.","year":"2023","unstructured":"J. Zhao , M. Qu , C. Li , H. Yan , Q. Liu , R. Li , X. Xie , and J. Tang . Learning on large-scale text-attributed graphs via variational inference . In The Eleventh International Conference on Learning Representations , 2023 . J. Zhao, M. Qu, C. Li, H. Yan, Q. Liu, R. Li, X. Xie, and J. Tang. Learning on large-scale text-attributed graphs via variational inference. In The Eleventh International Conference on Learning Representations, 2023."},{"key":"e_1_2_1_75_1","doi-asserted-by":"publisher","DOI":"10.1109\/IA351965.2020.00011"},{"key":"e_1_2_1_76_1","doi-asserted-by":"publisher","DOI":"10.14778\/3352063.3352127"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3611479.3611483","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,23]],"date-time":"2023-09-23T22:17:55Z","timestamp":1695507475000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3611479.3611483"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7]]},"references-count":76,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2023,7]]}},"alternative-id":["10.14778\/3611479.3611483"],"URL":"https:\/\/doi.org\/10.14778\/3611479.3611483","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2023,7]]},"assertion":[{"value":"2023-08-24","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}