{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T04:36:07Z","timestamp":1774931767176,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":41,"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:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Foundation of China","award":["62106216,62162064"],"award-info":[{"award-number":["62106216,62162064"]}]},{"name":"The Key Scientific and Technological Project of Yunnan Province","award":["No.202002AB080001-5,202102AB080019-2"],"award-info":[{"award-number":["No.202002AB080001-5,202102AB080019-2"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,8,25]]},"DOI":"10.1145\/3637528.3671719","type":"proceedings-article","created":{"date-parts":[[2024,8,25]],"date-time":"2024-08-25T04:54:55Z","timestamp":1724561695000},"page":"689-700","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Reserving-Masking-Reconstruction Model for Self-Supervised Heterogeneous Graph Representation"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2751-2589","authenticated-orcid":false,"given":"Haoran","family":"Duan","sequence":"first","affiliation":[{"name":"Yunnan University, Kunming, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4484-7428","authenticated-orcid":false,"given":"Cheng","family":"Xie","sequence":"additional","affiliation":[{"name":"Yunnan University, Kunming, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-8626-0608","authenticated-orcid":false,"given":"Linyu","family":"Li","sequence":"additional","affiliation":[{"name":"Yunnan University, Kunming, China"}]}],"member":"320","published-online":{"date-parts":[[2024,8,24]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbab275"},{"key":"e_1_3_2_2_2_1","volume-title":"Proceedings, Part XXXI (Lecture Notes in Computer Science","volume":"473","author":"Assran Mahmoud","year":"2022","unstructured":"Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Florian Bordes, Pascal Vincent, Armand Joulin, Mike Rabbat, and Nicolas Ballas. 2022. Masked Siamese Networks for Label-Efficient Learning. In Computer Vision - ECCV 2022 - 17th European Conference, Tel Aviv, Israel, October 23--27, 2022, Proceedings, Part XXXI (Lecture Notes in Computer Science, Vol. 13691). Springer, 456--473."},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3539597.3570484"},{"key":"e_1_3_2_2_4_1","volume-title":"Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13--18","volume":"1607","author":"Chen Ting","year":"2020","unstructured":"Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey E. Hinton. 2020. A Simple Framework for Contrastive Learning of Visual Representations. In Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13--18 July 2020, Virtual Event (Proceedings of Machine Learning Research, Vol. 119). PMLR, 1597--1607."},{"key":"e_1_3_2_2_5_1","volume-title":"Exploring Simple Siamese Representation Learning. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2021","author":"Chen Xinlei","year":"2021","unstructured":"Xinlei Chen and Kaiming He. 2021. Exploring Simple Siamese Representation Learning. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2021, virtual, June 19--25, 2021. Computer Vision Foundation \/ IEEE, 15750--15758."},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098036"},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2023.08.039"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122075"},{"key":"e_1_3_2_2_9_1","volume-title":"Fast Graph Representation Learning with PyTorch Geometric. In ICLR Workshop on Representation Learning on Graphs and Manifolds.","author":"Fey Matthias","unstructured":"Matthias Fey and Jan E. Lenssen. 2019. Fast Graph Representation Learning with PyTorch Geometric. In ICLR Workshop on Representation Learning on Graphs and Manifolds."},{"key":"e_1_3_2_2_10_1","volume-title":"MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding. In WWW '20: The Web Conference 2020","author":"Fu Xinyu","year":"2020","unstructured":"Xinyu Fu, Jiani Zhang, Ziqiao Meng, and Irwin King. 2020. MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding. In WWW '20: The Web Conference 2020, Taipei, Taiwan, April 20--24, 2020,, Yennun Huang, Irwin King, Tie-Yan Liu, and Maarten van Steen (Eds.). ACM \/ IW3C2, 2331--2341."},{"key":"e_1_3_2_2_11_1","volume-title":"Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020","author":"Grill Jean-Bastien","year":"2020","unstructured":"Jean-Bastien Grill, Florian Strub, Florent Altch\u00e9, Corentin Tallec, Pierre H. Richemond, Elena Buchatskaya, Carl Doersch, Bernardo \u00c1vila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, R\u00e9mi Munos, and Michal Valko. 2020. Bootstrap Your Own Latent - A New Approach to Self-Supervised Learning. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6--12, 2020, virtual."},{"key":"e_1_3_2_2_12_1","volume-title":"Masked Autoencoders Are Scalable Vision Learners. In IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022","author":"He Kaiming","year":"2022","unstructured":"Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Doll\u00e1r, and Ross B. Girshick. 2022. Masked Autoencoders Are Scalable Vision Learners. In IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, LA, USA, June 18--24, 2022. IEEE, 15979--15988."},{"key":"e_1_3_2_2_13_1","volume-title":"Momentum Contrast for Unsupervised Visual Representation Learning. In 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020","author":"He Kaiming","year":"2020","unstructured":"Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross B. Girshick. 2020. Momentum Contrast for Unsupervised Visual Representation Learning. In 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13--19, 2020. Computer Vision Foundation \/ IEEE, 9726--9735."},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.123"},{"key":"e_1_3_2_2_15_1","volume-title":"GraphMAE: Self-Supervised Masked Graph Autoencoders. In KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","author":"Hou Zhenyu","year":"2022","unstructured":"Zhenyu Hou, Xiao Liu, Yukuo Cen, Yuxiao Dong, Hongxia Yang, Chunjie Wang, and Jie Tang. 2022. GraphMAE: Self-Supervised Masked Graph Autoencoders. In KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14 - 18, 2022, Aidong Zhang and Huzefa Rangwala (Eds.). ACM, 594--604."},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330970"},{"key":"e_1_3_2_2_17_1","volume-title":"GPT-GNN: Generative Pre-Training of Graph Neural Networks. In KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","author":"Hu Ziniu","year":"2020","unstructured":"Ziniu Hu, Yuxiao Dong, Kuansan Wang, Kai-Wei Chang, and Yizhou Sun. 2020. GPT-GNN: Generative Pre-Training of Graph Neural Networks. In KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Virtual Event, CA, USA, August 23--27, 2020. ACM, 1857--1867."},{"key":"e_1_3_2_2_18_1","volume-title":"Heterogeneous Graph Transformer. In WWW '20: The Web Conference 2020","author":"Hu Ziniu","year":"2020","unstructured":"Ziniu Hu, Yuxiao Dong, Kuansan Wang, and Yizhou Sun. 2020. Heterogeneous Graph Transformer. In WWW '20: The Web Conference 2020, Taipei, Taiwan, April 20--24, 2020, Yennun Huang, Irwin King, Tie-Yan Liu, and Maarten van Steen (Eds.). ACM \/ IW3C2, 2704--2710."},{"key":"e_1_3_2_2_19_1","volume-title":"Pre-training on Large-Scale Heterogeneous Graph. In KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","author":"Jiang Xunqiang","year":"2021","unstructured":"Xunqiang Jiang, Tianrui Jia, Yuan Fang, Chuan Shi, Zhe Lin, and Hui Wang. 2021. Pre-training on Large-Scale Heterogeneous Graph. In KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Virtual Event, Singapore, August 14--18, 2021, Feida Zhu, Beng Chin Ooi, and Chunyan Miao (Eds.). ACM, 756--766."},{"key":"e_1_3_2_2_20_1","volume-title":"Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, CIKM 2023","author":"Lee Eric Wonhee","year":"2023","unstructured":"Eric Wonhee Lee and Joyce C. Ho. 2023. PGB: A PubMed Graph Benchmark for Heterogeneous Network Representation Learning. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, CIKM 2023, Birmingham, United Kingdom, October 21--25, 2023,, Ingo Frommholz, Frank Hopfgartner, Mark Lee, Michael Oakes, Mounia Lalmas, Min Zhang, and Rodrygo L. T. Santos (Eds.). ACM, 5331--5335."},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467350"},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5985"},{"key":"e_1_3_2_2_23_1","volume-title":"Heterogeneous deep graph infomax. arXiv preprint arXiv:1911.08538","author":"Ren Yuxiang","year":"2019","unstructured":"Yuxiang Ren, Bo Liu, Chao Huang, Peng Dai, Liefeng Bo, and Jiawei Zhang. 2019. Heterogeneous deep graph infomax. arXiv preprint arXiv:1911.08538 (2019)."},{"key":"e_1_3_2_2_24_1","volume-title":"ESWC 2018, Heraklion, Crete, Greece, June 3--7, 2018, Proceedings (Lecture Notes in Computer Science","volume":"607","author":"Schlichtkrull Michael Sejr","unstructured":"Michael Sejr Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, and Max Welling. 2018. Modeling Relational Data with Graph Convolutional Networks. In The Semantic Web - 15th International Conference, ESWC 2018, Heraklion, Crete, Greece, June 3--7, 2018, Proceedings (Lecture Notes in Computer Science, Vol. 10843). Springer, 593--607."},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/1401890.1402008"},{"key":"e_1_3_2_2_26_1","volume-title":"Chawla","author":"Tian Yijun","year":"2023","unstructured":"Yijun Tian, Kaiwen Dong, Chunhui Zhang, Chuxu Zhang, and Nitesh V. Chawla. 2023. Heterogeneous Graph Masked Autoencoders. In Thirty-Seventh AAAI Conference on Artificial Intelligence, AAAI 2023, Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence, IAAI 2023, Thirteenth Symposium on Educational Advances in Artificial Intelligence, EAAI 2023, Washington, DC, USA, February 7--14, 2023, Brian Williams, Yiling Chen, and Jennifer Neville (Eds.). AAAI Press, 9997--10005."},{"key":"e_1_3_2_2_27_1","volume-title":"Proceedings, Part XI (Lecture Notes in Computer Science","volume":"794","author":"Tian Yonglong","year":"2020","unstructured":"Yonglong Tian, Dilip Krishnan, and Phillip Isola. 2020. Contrastive Multiview Coding. In Computer Vision - ECCV 2020 - 16th European Conference, Glasgow, UK, August 23--28, 2020, Proceedings, Part XI (Lecture Notes in Computer Science, Vol. 12356). Springer, 776--794."},{"key":"e_1_3_2_2_28_1","volume-title":"Augmentations in Graph Contrastive Learning: Current Methodological Flaws & Towards Better Practices. In WWW '22: The ACM Web Conference 2022","author":"Trivedi Puja","year":"2022","unstructured":"Puja Trivedi, Ekdeep Singh Lubana, Yujun Yan, Yaoqing Yang, and Danai Koutra. 2022. Augmentations in Graph Contrastive Learning: Current Methodological Flaws & Towards Better Practices. In WWW '22: The ACM Web Conference 2022, Virtual Event, Lyon, France, April 25 - 29, 2022,, Fr\u00e9d\u00e9rique Laforest, Rapha\u00ebl Troncy, Elena Simperl, Deepak Agarwal, Aristides Gionis, Ivan Herman, and Lionel M\u00e9dini (Eds.). ACM, 1538--1549."},{"key":"e_1_3_2_2_29_1","volume-title":"Graph Attention Networks. In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings.","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 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings."},{"key":"e_1_3_2_2_30_1","volume-title":"Deep Graph Infomax. In 7th International Conference on Learning Representations, ICLR 2019","author":"Velickovic Petar","year":"2019","unstructured":"Petar Velickovic, William Fedus, William L. Hamilton, Pietro Li\u00f2, Yoshua Bengio, and R. Devon Hjelm. 2019. Deep Graph Infomax. In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6--9, 2019."},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1162\/qss_a_00021"},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2022.3177455"},{"key":"e_1_3_2_2_33_1","volume-title":"Heterogeneous Graph Attention Network. In The World Wide Web Conference, WWW 2019","author":"Wang Xiao","year":"2019","unstructured":"Xiao Wang, Houye Ji, Chuan Shi, Bai Wang, Yanfang Ye, Peng Cui, and Philip S. Yu. 2019. Heterogeneous Graph Attention Network. In The World Wide Web Conference, WWW 2019, San Francisco, CA, USA, May 13--17, 2019,, Ling Liu, Ryen W. White, Amin Mantrach, Fabrizio Silvestri, Julian J. McAuley, Ricardo Baeza-Yates, and Leila Zia (Eds.). ACM, 2022--2032."},{"key":"e_1_3_2_2_34_1","volume-title":"Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learning. In KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","author":"Wang Xiao","year":"2021","unstructured":"Xiao Wang, Nian Liu, Hui Han, and Chuan Shi. 2021. Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learning. In KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Virtual Event, Singapore, August 14--18, 2021,, Feida Zhu, Beng Chin Ooi, and Chunyan Miao (Eds.). ACM, 1726--1736."},{"key":"e_1_3_2_2_35_1","volume-title":"Masked Feature Prediction for Self-Supervised Visual Pre-Training. In IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022","author":"Wei Chen","year":"2022","unstructured":"Chen Wei, Haoqi Fan, Saining Xie, Chao-Yuan Wu, Alan L. Yuille, and Christoph Feichtenhofer. 2022. Masked Feature Prediction for Self-Supervised Visual Pre-Training. In IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, LA, USA, June 18--24, 2022. IEEE, 14648--14658."},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i9.26283"},{"key":"e_1_3_2_2_37_1","volume-title":"Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems","author":"Yang Yaming","year":"2022","unstructured":"Yaming Yang, Ziyu Guan, Zhe Wang, Wei Zhao, Cai Xu, Weigang Lu, and Jianbin Huang. 2022. Self-supervised Heterogeneous Graph Pre-training Based on Structural Clustering. In Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, NeurIPS 2022, New Orleans, LA, USA, November 28 - December 9, 2022, Sanmi Koyejo, S. Mohamed, A. Agarwal, Danielle Belgrave, K. Cho, and A. Oh (Eds.)."},{"key":"e_1_3_2_2_38_1","volume-title":"Scalable graph neural networks for heterogeneous graphs. arXiv preprint arXiv:2011.09679","author":"Yu Lingfan","year":"2020","unstructured":"Lingfan Yu, Jiajun Shen, Jinyang Li, and Adam Lerer. 2020. Scalable graph neural networks for heterogeneous graphs. arXiv preprint arXiv:2011.09679 (2020)."},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330785"},{"key":"e_1_3_2_2_40_1","volume-title":"Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021","author":"Zhang Hengrui","year":"2021","unstructured":"Hengrui Zhang, Qitian Wu, Junchi Yan, David Wipf, and Philip S. Yu. 2021. From Canonical Correlation Analysis to Self-supervised Graph Neural Networks. In Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6--14, 2021, virtual, Marc'Aurelio Ranzato, Alina Beygelzimer, Yann N. Dauphin, Percy Liang, and Jennifer Wortman Vaughan (Eds.). 76--89."},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/190"}],"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\/3637528.3671719","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3637528.3671719","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:06:01Z","timestamp":1750291561000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3671719"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,24]]},"references-count":41,"alternative-id":["10.1145\/3637528.3671719","10.1145\/3637528"],"URL":"https:\/\/doi.org\/10.1145\/3637528.3671719","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"}}]}}