{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T05:50:47Z","timestamp":1777614647196,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":53,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,8,24]],"date-time":"2024-08-24T00:00:00Z","timestamp":1724457600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,8,25]]},"DOI":"10.1145\/3637528.3671788","type":"proceedings-article","created":{"date-parts":[[2024,8,25]],"date-time":"2024-08-25T04:55:12Z","timestamp":1724561712000},"page":"1746-1757","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Customizing Graph Neural Network for CAD Assembly Recommendation"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-6382-6486","authenticated-orcid":false,"given":"Fengqi","family":"Liang","sequence":"first","affiliation":[{"name":"Beijing University of Post and Telecommunication, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0320-8718","authenticated-orcid":false,"given":"Huan","family":"Zhao","sequence":"additional","affiliation":[{"name":"4Paradigm Inc., Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9257-9109","authenticated-orcid":false,"given":"Yuhan","family":"Quan","sequence":"additional","affiliation":[{"name":"4Paradigm Inc., Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5618-0010","authenticated-orcid":false,"given":"Wei","family":"Fang","sequence":"additional","affiliation":[{"name":"Beijing University of Post and Telecommunication, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3734-0266","authenticated-orcid":false,"given":"Chuan","family":"Shi","sequence":"additional","affiliation":[{"name":"Beijing University of Post and Telecommunication, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2024,8,24]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Random search for hyper-parameter optimization. Journal of machine learning research","author":"Bergstra James","year":"2012","unstructured":"James Bergstra and Yoshua Bengio. 2012. Random search for hyper-parameter optimization. Journal of machine learning research (2012)."},{"key":"e_1_3_2_2_2_1","unstructured":"Shaked Brody Uri Alon and Eran Yahav. 2022. How Attentive are Graph Attention Networks?. In ICLR."},{"key":"e_1_3_2_2_3_1","volume-title":"Steven J Kiddle, Dino Oglic, and Pietro Li\u00f2.","author":"Buterez David","year":"2022","unstructured":"David Buterez, Jon Paul Janet, Steven J Kiddle, Dino Oglic, and Pietro Li\u00f2. 2022. Graph Neural Networks with Adaptive Readouts. In NeurIPS. 19746--19758."},{"key":"e_1_3_2_2_4_1","unstructured":"Xiangning Chen and Cho-Jui Hsieh. 2020. Stabilizing differentiable architecture search via perturbation-based regularization. In ICML. 1554--1565."},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"crossref","unstructured":"Xuanyi Dong and Yi Yang. 2019. Searching for a robust neural architecture in four gpu hours. In CVPR. 1761--1770.","DOI":"10.1109\/CVPR.2019.00186"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.5555\/3322706.3361996"},{"key":"e_1_3_2_2_7_1","unstructured":"Yaroslav Ganin Sergey Bartunov Yujia Li Ethan Keller and Stefano Saliceti. 2021. Computer-aided design as language. In NeurIPS. 5885--5897."},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"crossref","unstructured":"Yang Gao Hong Yang Peng Zhang Chuan Zhou and Yue Hu. 2021. Graph neural architecture search. In IJCAI. 1403--1409.","DOI":"10.24963\/ijcai.2020\/195"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3214832"},{"key":"e_1_3_2_2_10_1","unstructured":"Will Hamilton Zhitao Ying and Jure Leskovec. 2017. Inductive representation learning on large graphs. In NeurIPS. 1024--1034."},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"crossref","unstructured":"ZHAO Huan YAO Quanming and TU Weiwei. 2021. Search to aggregate neighborhood for graph neural network. In ICDE. 552--563.","DOI":"10.1109\/ICDE51399.2021.00054"},{"key":"e_1_3_2_2_12_1","unstructured":"AJAY KUMAR JAISWAL Peihao Wang Tianlong Chen Justin F Rousseau Ying Ding and Zhangyang Wang. 2022. Old can be Gold: Better Gradient Flow can Make Vanilla-GCNs Great Again. In NeurIPS."},{"key":"e_1_3_2_2_13_1","unstructured":"Eric Jang Shixiang Gu and Ben Poole. 2017. Categorical Reparameterization with Gumbel-Softmax. In ICLR."},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3478513.3480562","article-title":"Automate: A dataset and learning approach for automatic mating of cad assemblies","volume":"40","author":"Jones Benjamin","year":"2021","unstructured":"Benjamin Jones, Dalton Hildreth, Duowen Chen, Ilya Baran, Vladimir G Kim, and Adriana Schulz. 2021. Automate: A dataset and learning approach for automatic mating of cad assemblies. ACM Transactions on Graphics (TOG), Vol. 40, 6 (2021), 1--18.","journal-title":"ACM Transactions on Graphics (TOG)"},{"key":"e_1_3_2_2_15_1","volume-title":"Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980","author":"Kingma Diederik P","year":"2014","unstructured":"Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)."},{"key":"e_1_3_2_2_16_1","unstructured":"Thomas N Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In ICLR."},{"key":"e_1_3_2_2_17_1","unstructured":"Carola Lenzen Alexander Schiendorfer and Wolfgang Reif. 2022. A recommendation system for CAD assembly modeling based on graph neural networks. In ECML-PKDD."},{"key":"e_1_3_2_2_18_1","volume-title":"Deepgcns: Can gcns go as deep as cnns?. In CVPR. 9267--9276.","author":"Li Guohao","year":"2019","unstructured":"Guohao Li, Matthias Muller, Ali Thabet, and Bernard Ghanem. 2019. Deepgcns: Can gcns go as deep as cnns?. In CVPR. 9267--9276."},{"key":"e_1_3_2_2_19_1","unstructured":"Qimai Li Zhichao Han and Xiao-Ming Wu. 2018. Deeper insights into graph convolutional networks for semi-supervised learning. In AAAI."},{"key":"e_1_3_2_2_20_1","volume-title":"Design and evaluation of a command recommendation system for software applications. ACM Transactions on Computer-Human Interaction (TOCHI)","author":"Li Wei","year":"2011","unstructured":"Wei Li, Justin Matejka, Tovi Grossman, Joseph A Konstan, and George Fitzmaurice. 2011. Design and evaluation of a command recommendation system for software applications. ACM Transactions on Computer-Human Interaction (TOCHI) (2011), 1--35."},{"key":"e_1_3_2_2_21_1","volume-title":"Autograph: Automated graph neural network. In ICONIP. 189--201.","author":"Li Yaoman","year":"2020","unstructured":"Yaoman Li and Irwin King. 2020. Autograph: Automated graph neural network. In ICONIP. 189--201."},{"key":"e_1_3_2_2_22_1","volume-title":"Zemel","author":"Li Yujia","year":"2016","unstructured":"Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard S. Zemel. 2016. Gated Graph Sequence Neural Networks. In ICLR."},{"key":"e_1_3_2_2_23_1","volume-title":"Learning deep generative models of graphs. arXiv preprint arXiv:1803.03324","author":"Li Yujia","year":"2018","unstructured":"Yujia Li, Oriol Vinyals, Chris Dyer, Razvan Pascanu, and Peter Battaglia. 2018. Learning deep generative models of graphs. arXiv preprint arXiv:1803.03324 (2018)."},{"key":"e_1_3_2_2_24_1","volume-title":"Darts: Differentiable architecture search. In ICLR.","author":"Liu Hanxiao","year":"2019","unstructured":"Hanxiao Liu, Karen Simonyan, and Yiming Yang. 2019. Darts: Differentiable architecture search. In ICLR."},{"key":"e_1_3_2_2_25_1","volume-title":"Content-based CAD assembly model retrieval: Survey and future challenges. Computer-Aided Design","author":"Lupinetti Katia","year":"2019","unstructured":"Katia Lupinetti, Jean-Philippe Pernot, Marina Monti, and Franca Giannini. 2019. Content-based CAD assembly model retrieval: Survey and future challenges. Computer-Aided Design (2019), 62--81."},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"crossref","unstructured":"Weijian Ma Minyang Xu Xueyang Li and Xiangdong Zhou. 2023. MultiCAD: Contrastive Representation Learning for Multi-modal 3D Computer-Aided Design Models. In CIKM. 1766--1776.","DOI":"10.1145\/3583780.3614982"},{"key":"e_1_3_2_2_27_1","unstructured":"Tom\u00e1s Mikolov Kai Chen Greg Corrado and Jeffrey Dean. 2013. Efficient Estimation of Word Representations in Vector Space. In ICLR."},{"key":"e_1_3_2_2_28_1","volume-title":"Mixing patterns in networks. Phys. Rev. E","author":"Newman M. E. J.","year":"2003","unstructured":"M. E. J. Newman. 2003. Mixing patterns in networks. Phys. Rev. E (2003), 026126."},{"key":"e_1_3_2_2_29_1","volume-title":"Pytorch: An imperative style, high-performance deep learning library. In NeurIPS. 8024--8035.","author":"Paszke Adam","year":"2019","unstructured":"Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. In NeurIPS. 8024--8035."},{"key":"e_1_3_2_2_30_1","unstructured":"Yuhan Quan Huan Zhao Jinfeng Yi and Yuqiang Chen. 2024. Self-supervised Graph Neural Network for Mechanical CAD Retrieval. arxiv: 2406.08863"},{"key":"e_1_3_2_2_31_1","volume-title":"Computer aided design and manufacturing. PHI Learning Pvt","author":"Sarcar MMM","unstructured":"MMM Sarcar, K Mallikarjuna Rao, and K Lalit Narayan. 2008. Computer aided design and manufacturing. PHI Learning Pvt. Ltd."},{"key":"e_1_3_2_2_32_1","volume-title":"Multivariate Density Estimation: Theory, Practice, and Visualization","author":"Scott David W.","unstructured":"David W. Scott. 1992. Multivariate Density Estimation: Theory, Practice, and Visualization. Wiley."},{"key":"e_1_3_2_2_33_1","unstructured":"Petar Velivckovi\u0107 Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Lio and Yoshua Bengio. 2018. Graph attention networks. In ICLR."},{"key":"e_1_3_2_2_34_1","volume-title":"ICLR workshop.","author":"Wang Minjie Yu","year":"2019","unstructured":"Minjie Yu Wang. 2019. Deep graph library: Towards efficient and scalable deep learning on graphs. In ICLR workshop."},{"key":"e_1_3_2_2_35_1","unstructured":"Ruochen Wang Minhao Cheng Xiangning Chen Xiaocheng Tang and Cho-Jui Hsieh. 2021. Rethinking Architecture Selection in Differentiable NAS. In ICLR."},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"crossref","unstructured":"Zhenyi Wang Huan Zhao and Chuan Shi. 2022. Profiling the Design Space for Graph Neural Networks based Collaborative Filtering. In WSDM. 1109--1119.","DOI":"10.1145\/3488560.3498520"},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"crossref","unstructured":"Lanning Wei Zhiqiang He Huan Zhao and Quanming Yao. 2023. Search to capture long-range dependency with stacking gnns for graph classification. In TheWebConf. 588--598.","DOI":"10.1145\/3543507.3583486"},{"key":"e_1_3_2_2_38_1","unstructured":"Lanning Wei Huan Zhao and Zhiqiang He. 2022. Designing the topology of graph neural networks: A novel feature fusion perspective. In TheWebConf. 1381--1391."},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/3584945"},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"crossref","unstructured":"Lanning Wei Huan Zhao Quanming Yao and Zhiqiang He. 2021. Pooling architecture search for graph classification. In CIKM. 2091--2100.","DOI":"10.1145\/3459637.3482285"},{"key":"e_1_3_2_2_41_1","volume-title":"Hang Chu, Yunsheng Tian, Yifei Li, Daniele Grandi, Aditya Sanghi, Linh Tran, Joseph G Lambourne, Armando Solar-Lezama, et al.","author":"Willis Karl DD","year":"2022","unstructured":"Karl DD Willis, Pradeep Kumar Jayaraman, Hang Chu, Yunsheng Tian, Yifei Li, Daniele Grandi, Aditya Sanghi, Linh Tran, Joseph G Lambourne, Armando Solar-Lezama, et al. 2022. Joinable: Learning bottom-up assembly of parametric cad joints. In CVPR. 15849--15860."},{"key":"e_1_3_2_2_42_1","volume-title":"Hang Chu, Yunsheng Tian, Yifei Li, Daniele Grandi, Aditya Sanghi, Linh Tran, Joseph G Lambourne, Armando Solar-Lezama, and Wojciech Matusik.","author":"Willis Karl DD","year":"2021","unstructured":"Karl DD Willis, Pradeep Kumar Jayaraman, Hang Chu, Yunsheng Tian, Yifei Li, Daniele Grandi, Aditya Sanghi, Linh Tran, Joseph G Lambourne, Armando Solar-Lezama, and Wojciech Matusik. 2021. JoinABLe: Learning Bottom-up Assembly of Parametric CAD Joints. arXiv preprint arXiv:2111.12772 (2021)."},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/4235.585893"},{"key":"e_1_3_2_2_44_1","unstructured":"Sirui Xie Hehui Zheng Chunxiao Liu and Liang Lin. 2018. SNAS: stochastic neural architecture search. In ICLR."},{"key":"e_1_3_2_2_45_1","unstructured":"Keyulu Xu Weihua Hu Jure Leskovec and Stefanie Jegelka. 2019. How powerful are graph neural networks?. In ICLR."},{"key":"e_1_3_2_2_46_1","unstructured":"Keyulu Xu Chengtao Li Yonglong Tian Tomohiro Sonobe Ken-ichi Kawarabayashi and Stefanie Jegelka. 2018. Representation Learning on Graphs with Jumping Knowledge Networks. In ICML. 5453--5462."},{"key":"e_1_3_2_2_47_1","unstructured":"Jiaxuan You Zhitao Ying and Jure Leskovec. 2020. Design space for graph neural networks. In NeurIPS. 17009--17021."},{"key":"e_1_3_2_2_48_1","unstructured":"Kaicheng Yu Christian Suito Martin Jaggi Claudiu-Cristian Musat and Mathieu Salzmann. 2020. Evaluating the search phase of neural architecture search. In ICLR."},{"key":"e_1_3_2_2_49_1","unstructured":"Arber Zela Thomas Elsken Tonmoy Saikia Yassine Marrakchi Thomas Brox and Frank Hutter. 2020. Understanding and Robustifying Differentiable Architecture Search. In ICLR."},{"key":"e_1_3_2_2_50_1","unstructured":"Guanqi Zhan Qingnan Fan Kaichun Mo Lin Shao Baoquan Chen Leonidas J Guibas Hao Dong et al. 2020. Generative 3d part assembly via dynamic graph learning. In NeurIPS. 6315--6326."},{"key":"e_1_3_2_2_51_1","doi-asserted-by":"crossref","unstructured":"Tianyu Zhao Cheng Yang Yibo Li Quan Gan Zhenyi Wang Fengqi Liang Huan Zhao Yingxia Shao Xiao Wang and Chuan Shi. 2022. Space4hgnn: a novel modularized and reproducible platform to evaluate heterogeneous graph neural network. In SIGIR. 2776--2789.","DOI":"10.1145\/3477495.3531720"},{"key":"e_1_3_2_2_52_1","unstructured":"Barret Zoph and Quoc V Le. 2017. Neural architecture search with reinforcement learning. In ICLR."},{"key":"e_1_3_2_2_53_1","doi-asserted-by":"crossref","unstructured":"Barret Zoph Vijay Vasudevan Jonathon Shlens and Quoc V Le. 2018. Learning transferable architectures for scalable image recognition. In CVPR. 8697--8710.","DOI":"10.1109\/CVPR.2018.00907"}],"event":{"name":"KDD '24: The 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Barcelona Spain","acronym":"KDD '24","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3671788","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3637528.3671788","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:04:14Z","timestamp":1750291454000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3671788"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,24]]},"references-count":53,"alternative-id":["10.1145\/3637528.3671788","10.1145\/3637528"],"URL":"https:\/\/doi.org\/10.1145\/3637528.3671788","relation":{},"subject":[],"published":{"date-parts":[[2024,8,24]]},"assertion":[{"value":"2024-08-24","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}