{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T00:18:13Z","timestamp":1782519493064,"version":"3.54.5"},"publisher-location":"New York, NY, USA","reference-count":24,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,6,23]],"date-time":"2024-06-23T00:00:00Z","timestamp":1719100800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"NSFC","award":["62072019"],"award-info":[{"award-number":["62072019"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,6,23]]},"DOI":"10.1145\/3649329.3656504","type":"proceedings-article","created":{"date-parts":[[2024,11,7]],"date-time":"2024-11-07T19:27:22Z","timestamp":1731007642000},"page":"1-6","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["GNNavigator: Towards Adaptive Training of Graph Neural Networks via Automatic Guideline Exploration"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-6018-8414","authenticated-orcid":false,"given":"Tong","family":"Qiao","sequence":"first","affiliation":[{"name":"Beihang University, Beijing, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8424-7040","authenticated-orcid":false,"given":"Jianlei","family":"Yang","sequence":"additional","affiliation":[{"name":"Beihang University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-2785-4480","authenticated-orcid":false,"given":"Yingjie","family":"Qi","sequence":"additional","affiliation":[{"name":"Beihang University, Beijing, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-1440-1493","authenticated-orcid":false,"given":"Ao","family":"Zhou","sequence":"additional","affiliation":[{"name":"Beihang University, Beijing, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1742-0090","authenticated-orcid":false,"given":"Chen","family":"Bai","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region of China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6406-4810","authenticated-orcid":false,"given":"Bei","family":"Yu","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, Shatin, N\/A, Hong Kong Special Administrative Region of China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8088-0404","authenticated-orcid":false,"given":"Weisheng","family":"Zhao","sequence":"additional","affiliation":[{"name":"Beihang University, Beijing, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3473-9703","authenticated-orcid":false,"given":"Chunming","family":"Hu","sequence":"additional","affiliation":[{"name":"Beihang University, Beijing, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,11,7]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Proceedings of ICLR","author":"Thomas","year":"2017","unstructured":"Thomas N. Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. In Proceedings of ICLR, 2017."},{"key":"e_1_3_2_1_2_1","volume-title":"Proceedings of ICLR","author":"Velickovic Petar","year":"2018","unstructured":"Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, Yoshua Bengio, et al. Graph attention networks. In Proceedings of ICLR, 2018."},{"key":"e_1_3_2_1_3_1","volume-title":"Proceedings of SoCC","author":"Lin Zhiqi","year":"2020","unstructured":"Zhiqi Lin, Cheng Li, Youshan Miao, Yunxin Liu, and Yinlong Xu. Pagraph: Scaling gnn training on large graphs via computation-aware caching. In Proceedings of SoCC, 2020."},{"key":"e_1_3_2_1_4_1","volume-title":"Proceedings of NSDI","author":"Liu Tianfeng","year":"2023","unstructured":"Tianfeng Liu, Yangrui Chen, Dan Li, Chuan Wu, Yibo Zhu, Jun He, Yanghua Peng, Hongzheng Chen, et al. BGL:GPU-efficient GNN training by optimizing graph data I\/O and preprocessing. In Proceedings of NSDI, 2023."},{"key":"e_1_3_2_1_5_1","volume-title":"Proceedings of ICLR","author":"Chen Jie","year":"2018","unstructured":"Jie Chen, Tengfei Ma, and Cao Xiao. Fastgcn: Fast learning with graph convolutional networks via importance sampling. In Proceedings of ICLR, 2018."},{"key":"e_1_3_2_1_6_1","volume-title":"Proceedings of ICLR","author":"Zeng Hanqing","year":"2019","unstructured":"Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, and Viktor Prasanna. GraphSAINT: Graph sampling based inductive learning method. In Proceedings of ICLR, 2019."},{"key":"e_1_3_2_1_7_1","volume-title":"Proceedings of ECML PKDD","author":"Liu Xin","year":"2022","unstructured":"Xin Liu, Mingyu Yan, Shuhan Song, Zhengyang Lv, Wenming Li, Guangyu Sun, et al. GNNSampler: Bridging the gap between sampling algorithms of gnn and hardware. In Proceedings of ECML PKDD, 2022."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3400302.3415610"},{"key":"e_1_3_2_1_9_1","volume-title":"Proceedings of OSDI","author":"Wang Yuke","year":"2021","unstructured":"Yuke Wang, Boyuan Feng, Gushu Li, Shuangchen Li, Lei Deng, Yuan Xie, and Yufei Ding. GNNAdvisor: An adaptive and efficient runtime system for GNN acceleration on GPUs. In Proceedings of OSDI, 2021."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/HPCA53966.2022.00041"},{"key":"e_1_3_2_1_11_1","volume-title":"Machine Learning and Systems","author":"Kaler Tim","year":"2022","unstructured":"Tim Kaler, Nickolas Stathas, Anne Ouyang, Alexandros-Stavros Iliopoulos, Tao Schardl, et al. Accelerating training and inference of graph neural networks with fast sampling and pipelining. Machine Learning and Systems, 2022."},{"key":"e_1_3_2_1_12_1","volume-title":"Proceedings of EuroSys","author":"Yang Jianbang","year":"2022","unstructured":"Jianbang Yang, Dahai Tang, Xiaoniu Song, Lei Wang, Qiang Yin, Rong Chen, Wenyuan Yu, and Jingren Zhou. GNNLab: a factored system for sample-based GNN training over GPUs. In Proceedings of EuroSys, 2022."},{"key":"e_1_3_2_1_13_1","volume-title":"Proceedings of ICLR","author":"Liu Zirui","year":"2021","unstructured":"Zirui Liu, Kaixiong Zhou, Fan Yang, Li Li, Rui Chen, and Xia Hu. EXACT: Scalable graph neural networks training via extreme activation compression. In Proceedings of ICLR, 2021."},{"key":"e_1_3_2_1_14_1","volume-title":"Proceedings of CLUSTER","author":"Lizhi","year":"2021","unstructured":"Lizhi Zhang et al. 2PGraph: Accelerating GNN training over large graphs on GPU clusters. In Proceedings of CLUSTER, 2021."},{"key":"e_1_3_2_1_15_1","volume-title":"IEEE CAL","author":"Qi Yingjie","year":"2023","unstructured":"Yingjie Qi, Jianlei Yang, Ao Zhou, Tong Qiao, and Chunming Hu. Architectural implications of GNN aggregation programming abstractions. IEEE CAL, 2023."},{"key":"e_1_3_2_1_16_1","volume-title":"Proceedings of ICLR","author":"Fey Matthias","year":"2019","unstructured":"Matthias Fey and Jan Eric Lenssen. Fast graph representation learning with PyTorch Geometric. In Proceedings of ICLR, 2019."},{"key":"e_1_3_2_1_17_1","volume-title":"Deep graph library: A graph-centric, highly-performant package for graph neural networks. arXiv preprint arXiv:1909.01315","author":"Wang Minjie","year":"2019","unstructured":"Minjie Wang, Da Zheng, Zihao Ye, Quan Gan, Mufei Li, Xiang Song, et al. Deep graph library: A graph-centric, highly-performant package for graph neural networks. arXiv preprint arXiv:1909.01315, 2019."},{"key":"e_1_3_2_1_18_1","volume-title":"AliGraph: A comprehensive graph neural network platform. arXiv preprint arXiv:1902.08730","author":"Rong Zhu","year":"2019","unstructured":"Rong Zhu et al. AliGraph: A comprehensive graph neural network platform. arXiv preprint arXiv:1902.08730, 2019."},{"key":"e_1_3_2_1_19_1","volume-title":"HitGNN: High-throughput GNN training framework on CPU+ multi-FPGA heterogeneous platform. arXiv preprint arXiv:2303.01568","author":"Lin Yi-Chien","year":"2023","unstructured":"Yi-Chien Lin, Bingyi Zhang, and Viktor Prasanna. HitGNN: High-throughput GNN training framework on CPU+ multi-FPGA heterogeneous platform. arXiv preprint arXiv:2303.01568, 2023."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3490422.3502359"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/DAC56929.2023.10247875"},{"key":"e_1_3_2_1_22_1","volume-title":"Proceedings of ICCAD","author":"Bai Chen","year":"2021","unstructured":"Chen Bai, Qi Sun, Jianwang Zhai, Yuzhe Ma, Bei Yu, and Martin DF Wong. BOOM-Explorer: RISC-V BOOM microarchitecture design space exploration rramework. In Proceedings of ICCAD, 2021."},{"key":"e_1_3_2_1_23_1","volume-title":"Open graph benchmark: Datasets for machine learning on graphs. arXiv preprint arXiv:2005.00687","author":"Hu Weihua","year":"2020","unstructured":"Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec. Open graph benchmark: Datasets for machine learning on graphs. arXiv preprint arXiv:2005.00687, 2020."},{"key":"e_1_3_2_1_24_1","volume-title":"Proceedings of NIPS","author":"Adam","year":"2019","unstructured":"Adam Paszke et al. PyTorch: An imperative style, high-performance deep learning library. In Proceedings of NIPS, 2019."}],"event":{"name":"DAC '24: 61st ACM\/IEEE Design Automation Conference","location":"San Francisco CA USA","acronym":"DAC '24","sponsor":["SIGDA ACM Special Interest Group on Design Automation","IEEE-CEDA","SIGBED ACM Special Interest Group on Embedded Systems"]},"container-title":["Proceedings of the 61st ACM\/IEEE Design Automation Conference"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3649329.3656504","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3649329.3656504","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:17:55Z","timestamp":1750295875000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3649329.3656504"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,23]]},"references-count":24,"alternative-id":["10.1145\/3649329.3656504","10.1145\/3649329"],"URL":"https:\/\/doi.org\/10.1145\/3649329.3656504","relation":{},"subject":[],"published":{"date-parts":[[2024,6,23]]},"assertion":[{"value":"2024-11-07","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}