{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T05:47:28Z","timestamp":1777873648102,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":87,"publisher":"ACM","funder":[{"DOI":"10.13039\/501100006374","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62322213, 62461146205"],"award-info":[{"award-number":["62322213, 62461146205"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006374","name":"Beijing Nova Program","doi-asserted-by":"publisher","award":["20230484397, 20220484137"],"award-info":[{"award-number":["20230484397, 20220484137"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,8,3]]},"DOI":"10.1145\/3711896.3736888","type":"proceedings-article","created":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T13:32:14Z","timestamp":1754055134000},"page":"286-297","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["CompressGNN: Accelerating Graph Neural Network Training via Hierarchical Compression"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-8342-9504","authenticated-orcid":false,"given":"Zheng","family":"Chen","sequence":"first","affiliation":[{"name":"Renmin University of China, Beijing, China and Tsinghua University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1983-7321","authenticated-orcid":false,"given":"Feng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Renmin University of China, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-0316-8986","authenticated-orcid":false,"given":"Yifei","family":"Xia","sequence":"additional","affiliation":[{"name":"Peking University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7532-5550","authenticated-orcid":false,"given":"Wentao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Peking University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3880-308X","authenticated-orcid":false,"given":"Xiaowei","family":"Zhu","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4281-1018","authenticated-orcid":false,"given":"Wenguang","family":"Chen","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5757-9135","authenticated-orcid":false,"given":"Xiaoyong","family":"Du","sequence":"additional","affiliation":[{"name":"Renmin University of China, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,8,3]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Benoit Steiner, Paul A. Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng.","author":"Abadi Mart\u00edn","year":"2016","unstructured":"Mart\u00edn Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, Derek Gordon Murray, Benoit Steiner, Paul A. Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng. 2016. TensorFlow: A System for Large-Scale Machine Learning. In OSDI, USENIX Association, 265-283."},{"key":"e_1_3_2_2_2_1","first-page":"337","volume-title":"12th USENIX Symposium on Networked Systems Design and Implementation (NSDI 15)","author":"Agarwal Rachit","year":"2015","unstructured":"Rachit Agarwal, Anurag Khandelwal, and Ion Stoica. 2015. Succinct: Enabling queries on compressed data. In 12th USENIX Symposium on Networked Systems Design and Implementation (NSDI 15). 337-350."},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"crossref","unstructured":"Jasmijn Bastings Ivan Titov Wilker Aziz Diego Marcheggiani and Khalil Sima'an. 2017. Graph convolutional encoders for syntax-aware neural machine translation. arXiv preprint arXiv:1704.04675(2017).","DOI":"10.18653\/v1\/D17-1209"},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/1341531.1341547"},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671997"},{"key":"e_1_3_2_2_6_1","volume-title":"Mxnet: A flexible and efficient machine learning library for heterogeneous distributed systems. arXiv preprint arXiv:1512.01274(2015).","author":"Chen Tianqi","year":"2015","unstructured":"Tianqi Chen, Mu Li, Yutian Li, Min Lin, Naiyan Wang, Minjie Wang, Tianjun Xiao, Bing Xu, Chiyuan Zhang, and Zheng Zhang. 2015. Mxnet: A flexible and efficient machine learning library for heterogeneous distributed systems. arXiv preprint arXiv:1512.01274(2015)."},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3588684"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-75530-2_11"},{"key":"e_1_3_2_2_9_1","volume-title":"Advances in Neural Information Processing Systems","volume":"32","author":"Dai Hanjun","year":"2019","unstructured":"Hanjun Dai, Chengtao Li, Connor Coley, Bo Dai, and Le Song. 2019. Retrosynthesis prediction with conditional graph logic network. Advances in Neural Information Processing Systems, Vol. 32 (2019)."},{"key":"e_1_3_2_2_10_1","first-page":"6733","article-title":"VQ-GNN: A universal framework to scale up graph neural networks using vector quantization","volume":"34","author":"Ding Mucong","year":"2021","unstructured":"Mucong Ding, Kezhi Kong, Jingling Li, Chen Zhu, John Dickerson, Furong Huang, and Tom Goldstein. 2021. VQ-GNN: A universal framework to scale up graph neural networks using vector quantization. Advances in Neural Information Processing Systems, Vol. 34 (2021), 6733-6746.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671744"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671912"},{"key":"e_1_3_2_2_13_1","volume-title":"Advances in Neural Information Processing Systems","volume":"32","author":"Fazlyab Mahyar","year":"2019","unstructured":"Mahyar Fazlyab, Alexander Robey, Hamed Hassani, Manfred Morari, and George Pappas. 2019. Efficient and accurate estimation of lipschitz constants for deep neural networks. Advances in Neural Information Processing Systems, Vol. 32 (2019)."},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICTAI50040.2020.00198"},{"key":"e_1_3_2_2_15_1","unstructured":"Matthias Fey and Jan Eric Lenssen. 2019. Fast graph representation learning with PyTorch Geometric. arXiv preprint arXiv:1903.02428(2019)."},{"key":"e_1_3_2_2_16_1","first-page":"551","article-title":"P3: Distributed Deep Graph Learning at Scale. In OSDI","author":"Gandhi Swapnil","year":"2021","unstructured":"Swapnil Gandhi and Anand Padmanabha Iyer. 2021. P3: Distributed Deep Graph Learning at Scale. In OSDI, USENIX Association, 551-568.","journal-title":"USENIX Association"},{"key":"e_1_3_2_2_17_1","first-page":"518","article-title":"Similarity search in high dimensions via hashing","volume":"99","author":"Gionis Aristides","year":"1999","unstructured":"Aristides Gionis, Piotr Indyk, Rajeev Motwani, et al., 1999. Similarity search in high dimensions via hashing. In Vldb, Vol. 99. 518-529.","journal-title":"Vldb"},{"key":"e_1_3_2_2_18_1","first-page":"26578","article-title":"TREC: Transient Redundancy Elimination-based Convolution","volume":"35","author":"Guan Jiawei","year":"2022","unstructured":"Jiawei Guan, Feng Zhang, Jiesong Liu, Hsin-Hsuan Sung, Ruofan Wu, Xiaoyong Du, and Xipeng Shen. 2022. TREC: Transient Redundancy Elimination-based Convolution. Advances in Neural Information Processing Systems, Vol. 35 (2022), 26578-26589.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671792"},{"key":"e_1_3_2_2_20_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_21_1","doi-asserted-by":"publisher","DOI":"10.2307\/2346830"},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671895"},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/SC41405.2020.00075"},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/276698.276876"},{"key":"e_1_3_2_2_25_1","first-page":"187","article-title":"Improving the accuracy, scalability, and performance of graph neural networks with roc","volume":"2","author":"Jia Zhihao","year":"2020","unstructured":"Zhihao Jia, Sina Lin, Mingyu Gao, Matei Zaharia, and Alex Aiken. 2020. Improving the accuracy, scalability, and performance of graph neural networks with roc. Proceedings of Machine Learning and Systems, Vol. 2 (2020), 187-198.","journal-title":"Proceedings of Machine Learning and Systems"},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671890"},{"key":"e_1_3_2_2_27_1","volume-title":"Communication Optimization for Distributed Execution of Graph Neural Networks. In 2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS). IEEE, 512-523","author":"Kurt S\u00fcreyya Emre","year":"2023","unstructured":"S\u00fcreyya Emre Kurt, Jinghua Yan, Aravind Sukumaran-Rajam, Prashant Pandey, and P Sadayappan. 2023. Communication Optimization for Distributed Execution of Graph Neural Networks. In 2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS). IEEE, 512-523."},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671765"},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.14778\/3648160.3648176"},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671788"},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","unstructured":"Yaoyang Liu Zhen Zheng Feng Zhang Jincheng Feng Yiyang Fu Jidong Zhai Bingsheng He Xiao Zhang and Xiaoyong Du. 2025. A Comprehensive Taxonomy of Prompt Engineering Techniques for Large Language Models. Frontiers of Computer Science(2025). doi:10.1007\/s11704-025-50058-z","DOI":"10.1007\/s11704-025-50058-z"},{"key":"e_1_3_2_2_32_1","volume-title":"International Conference on Learning Representations.","author":"Liu Zirui","year":"2021","unstructured":"Zirui Liu, Kaixiong Zhou, Fan Yang, Li Li, Rui Chen, and Xia Hu. 2021. EXACT: Scalable graph neural networks training via extreme activation compression. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671699"},{"key":"e_1_3_2_2_34_1","first-page":"443","volume-title":"2019 USENIX Annual Technical Conference (USENIX ATC 19)","author":"Ma Lingxiao","year":"2019","unstructured":"Lingxiao Ma, Zhi Yang, Youshan Miao, Jilong Xue, Ming Wu, Lidong Zhou, and Yafei Dai. 2019. &#123;NeuGraph&#125;: Parallel Deep Neural Network Computation on Large Graphs. In 2019 USENIX Annual Technical Conference (USENIX ATC 19). 443-458."},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.14778\/3681954.3681968"},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"crossref","unstructured":"Diego Marcheggiani and Ivan Titov. 2017. Encoding sentences with graph convolutional networks for semantic role labeling. arXiv preprint arXiv:1703.04826(2017).","DOI":"10.18653\/v1\/D17-1159"},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3458817.3480856"},{"key":"e_1_3_2_2_38_1","unstructured":"Nvidia. 2025a. cuBLAS - NVIDIA Developer. https:\/\/developer.nvidia.com\/cublas."},{"key":"e_1_3_2_2_39_1","unstructured":"Nvidia. 2025b. CUDA Sparse Matrix library (cuSPARSE). https:\/\/developer.nvidia.com\/cusparse."},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.14778\/3648160.3648166"},{"key":"e_1_3_2_2_41_1","first-page":"8024","article-title":"PyTorch: An Imperative Style, High-Performance Deep Learning Library","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, Alban Desmaison, Andreas K\u00f6pf, Edward Z. Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019. PyTorch: An Imperative Style, High-Performance Deep Learning Library. In NeurIPS. 8024-8035.","journal-title":"NeurIPS."},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/LCSYS.2021.3050444"},{"key":"e_1_3_2_2_43_1","volume-title":"Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'18)","author":"Qiu Jiezhong","year":"2018","unstructured":"Jiezhong Qiu, Jian Tang, Hao Ma, Yuxiao Dong, Kuansan Wang, and Jie Tang. 2018. Deepinf: Modeling influence locality in large social networks. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'18)."},{"key":"e_1_3_2_2_44_1","volume-title":"Collective classification in network data. AI magazine","author":"Sen Prithviraj","year":"2008","unstructured":"Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad. 2008. Collective classification in network data. AI magazine, Vol. 29, 3 (2008), 93-93."},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.14778\/3685800.3685844"},{"key":"e_1_3_2_2_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671844"},{"key":"e_1_3_2_2_47_1","doi-asserted-by":"publisher","DOI":"10.14778\/3681954.3681976"},{"key":"e_1_3_2_2_48_1","volume-title":"The Eleventh International Conference on Learning Representations.","author":"Si Si","year":"2023","unstructured":"Si Si, Felix Yu, Ankit Singh Rawat, Cho-Jui Hsieh, and Sanjiv Kumar. 2023. Serving graph compression for graph neural networks. In The Eleventh International Conference on Learning Representations."},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.14778\/3681954.3682008"},{"key":"e_1_3_2_2_50_1","unstructured":"Shyam Anil Tailor Javier Fern\u00e1ndez-Marqu\u00e9s and Nicholas Donald Lane. 2021. Degree-Quant: Quantization-Aware Training for Graph Neural Networks. In ICLR OpenReview.net."},{"key":"e_1_3_2_2_51_1","volume-title":"Dorylus: Affordable, Scalable, and Accurate GNN Training with Distributed CPU Servers and Serverless Threads. In OSDI","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 OSDI, USENIX Association, 495-514."},{"key":"e_1_3_2_2_52_1","doi-asserted-by":"publisher","DOI":"10.5555\/3433701.3433794"},{"key":"e_1_3_2_2_53_1","unstructured":"Petar Veli\u010dkovi\u0107 Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Lio and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903(2017)."},{"key":"e_1_3_2_2_54_1","doi-asserted-by":"publisher","DOI":"10.1145\/3448016.3457328"},{"key":"e_1_3_2_2_55_1","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671791"},{"key":"e_1_3_2_2_56_1","volume-title":"Deep Graph Library: Towards Efficient and Scalable Deep Learning on Graphs. CoRR","author":"Wang Minjie","year":"2019","unstructured":"Minjie Wang, Lingfan Yu, Da Zheng, Quan Gan, Yu Gai, Zihao Ye, Mufei Li, Jinjing Zhou, Qi Huang, Chao Ma, Ziyue Huang, Qipeng Guo, Hao Zhang, Haibin Lin, Junbo Zhao, Jinyang Li, Alexander J. Smola, and Zheng Zhang. 2019. Deep Graph Library: Towards Efficient and Scalable Deep Learning on Graphs. CoRR, Vol. abs\/1909.01315 (2019). arXiv:1909.01315 http:\/\/arxiv.org\/abs\/1909.01315"},{"key":"e_1_3_2_2_57_1","doi-asserted-by":"publisher","DOI":"10.1145\/3626733"},{"key":"e_1_3_2_2_58_1","doi-asserted-by":"publisher","DOI":"10.1145\/3514221.3526134"},{"key":"e_1_3_2_2_59_1","first-page":"515","volume-title":"15th USENIX symposium on operating systems design and implementation (OSDI 21)","author":"Wang Yuke","year":"2021","unstructured":"Yuke Wang, Boyuan Feng, Gushu Li, Shuangchen Li, Lei Deng, Yuan Xie, and Yufei Ding. 2021. &#123;GNNAdvisor&#125;: An adaptive and efficient runtime system for &#123;GNN&#125; acceleration on &#123;GPUs&#125;. In 15th USENIX symposium on operating systems design and implementation (OSDI 21). 515-531."},{"key":"e_1_3_2_2_60_1","first-page":"149","volume-title":"2023 USENIX Annual Technical Conference (USENIX ATC 23)","author":"Wang Yuke","year":"2023","unstructured":"Yuke Wang, Boyuan Feng, Zheng Wang, Guyue Huang, and Yufei Ding. 2023b. &#123;TC-GNN&#125;: Bridging Sparse &#123;GNN&#125; Computation and Dense Tensor Cores on &#123;GPUs&#125;. In 2023 USENIX Annual Technical Conference (USENIX ATC 23). 149-164."},{"key":"e_1_3_2_2_61_1","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671864"},{"key":"e_1_3_2_2_62_1","volume-title":"J. International Conference on Learning Representations (ICLR","author":"Welling Max","year":"2016","unstructured":"Max Welling and Thomas N Kipf. 2016. Semi-supervised classification with graph convolutional networks. In J. International Conference on Learning Representations (ICLR 2017)."},{"key":"e_1_3_2_2_63_1","volume-title":"International conference on machine learning. PMLR, 6861-6871","author":"Wu Felix","year":"2019","unstructured":"Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger. 2019. Simplifying graph convolutional networks. In International conference on machine learning. PMLR, 6861-6871."},{"key":"e_1_3_2_2_64_1","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671706"},{"key":"e_1_3_2_2_65_1","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671472"},{"key":"e_1_3_2_2_66_1","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671731"},{"key":"e_1_3_2_2_67_1","doi-asserted-by":"publisher","DOI":"10.1145\/3492321.3519557"},{"key":"e_1_3_2_2_68_1","doi-asserted-by":"publisher","DOI":"10.1145\/3575693.3575725"},{"key":"e_1_3_2_2_69_1","doi-asserted-by":"publisher","DOI":"10.1145\/3514221.3517872"},{"key":"e_1_3_2_2_70_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219890"},{"key":"e_1_3_2_2_71_1","volume-title":"Graphsaint: Graph sampling based inductive learning method. arXiv preprint arXiv:1907.04931(2019).","author":"Zeng Hanqing","year":"2019","unstructured":"Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, and Viktor Prasanna. 2019. Graphsaint: Graph sampling based inductive learning method. arXiv preprint arXiv:1907.04931(2019)."},{"key":"e_1_3_2_2_72_1","doi-asserted-by":"publisher","DOI":"10.14778\/3415478.3415539"},{"key":"e_1_3_2_2_73_1","doi-asserted-by":"publisher","DOI":"10.1145\/3514221.3526130"},{"key":"e_1_3_2_2_74_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2021.3093234"},{"key":"e_1_3_2_2_75_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00778-020-00636-3"},{"key":"e_1_3_2_2_76_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCC.2025.3559346"},{"key":"e_1_3_2_2_77_1","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671946"},{"key":"e_1_3_2_2_78_1","doi-asserted-by":"publisher","DOI":"10.1145\/3448016.3457325"},{"key":"e_1_3_2_2_79_1","doi-asserted-by":"crossref","unstructured":"Wentao Zhang Zeang Sheng Ziqi Yin Yuezihan Jiang Yikuan Xia Jun Gao Zhi Yang and Bin Cui. 2022a. Model Degradation Hinders Deep Graph Neural Networks. arXiv preprint arXiv:2206.04361(2022).","DOI":"10.1145\/3534678.3539374"},{"key":"e_1_3_2_2_80_1","doi-asserted-by":"publisher","DOI":"10.1145\/3626246.3653399"},{"key":"e_1_3_2_2_81_1","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3672061"},{"key":"e_1_3_2_2_82_1","doi-asserted-by":"publisher","DOI":"10.1109\/IA351965.2020.00011"},{"key":"e_1_3_2_2_83_1","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671957"},{"key":"e_1_3_2_2_84_1","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3672026"},{"key":"e_1_3_2_2_85_1","doi-asserted-by":"publisher","DOI":"10.1145\/3575693.3575723"},{"key":"e_1_3_2_2_86_1","volume-title":"Aligraph: A comprehensive graph neural network platform. arXiv preprint arXiv:1902.08730(2019).","author":"Zhu Rong","year":"2019","unstructured":"Rong Zhu, Kun Zhao, Hongxia Yang, Wei Lin, Chang Zhou, Baole Ai, Yong Li, and Jingren Zhou. 2019. Aligraph: A comprehensive graph neural network platform. arXiv preprint arXiv:1902.08730(2019)."},{"key":"e_1_3_2_2_87_1","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3672029"}],"event":{"name":"KDD '25: The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Toronto ON Canada","acronym":"KDD '25","sponsor":["SIGKDD ACM Special Interest Group on Knowledge Discovery in Data","SIGMOD ACM Special Interest Group on Management of Data"]},"container-title":["Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3711896.3736888","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T18:08:41Z","timestamp":1777572521000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3711896.3736888"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,3]]},"references-count":87,"alternative-id":["10.1145\/3711896.3736888","10.1145\/3711896"],"URL":"https:\/\/doi.org\/10.1145\/3711896.3736888","relation":{},"subject":[],"published":{"date-parts":[[2025,8,3]]},"assertion":[{"value":"2025-08-03","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}