{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T03:40:21Z","timestamp":1771299621121,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":49,"publisher":"ACM","license":[{"start":{"date-parts":[[2025,4,22]],"date-time":"2025-04-22T00:00:00Z","timestamp":1745280000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/https:\/\/doi.org\/10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No.62476238"],"award-info":[{"award-number":["No.62476238"]}],"id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Zhejiang Province High-Level Talents Special Support Program Leading Talent of Technological Innovation of Ten-Thousands Talents Program","award":["No.2022R52046"],"award-info":[{"award-number":["No.2022R52046"]}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["No.LHZSD24F020001, No.LMS25F020012"],"award-info":[{"award-number":["No.LHZSD24F020001, No.LMS25F020012"]}]},{"name":"CCF-Baidu Open Fund","award":["No.CCF-BAIDU OF202410"],"award-info":[{"award-number":["No.CCF-BAIDU OF202410"]}]},{"name":"National Key Research and Development Project","award":["No.2022YFB2703100"],"award-info":[{"award-number":["No.2022YFB2703100"]}]},{"name":"Ningbo Natural Science Foundation","award":["No.2023J281"],"award-info":[{"award-number":["No.2023J281"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,4,28]]},"DOI":"10.1145\/3696410.3714851","type":"proceedings-article","created":{"date-parts":[[2025,4,22]],"date-time":"2025-04-22T22:47:11Z","timestamp":1745362031000},"page":"4494-4506","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Disentangled Condensation for Large-scale Graphs"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-8600-2687","authenticated-orcid":false,"given":"Zhenbang","family":"Xiao","sequence":"first","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-5193-1841","authenticated-orcid":false,"given":"Yu","family":"Wang","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0584-9129","authenticated-orcid":false,"given":"Shunyu","family":"Liu","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2556-9239","authenticated-orcid":false,"given":"Bingde","family":"Hu","sequence":"additional","affiliation":[{"name":"Bangsun Technology, Hangzhou, China and Zhejiang University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9560-563X","authenticated-orcid":false,"given":"Huiqiong","family":"Wang","sequence":"additional","affiliation":[{"name":"Zhejiang University, Ningbo, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2621-6048","authenticated-orcid":false,"given":"Mingli","family":"Song","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1190-9773","authenticated-orcid":false,"given":"Tongya","family":"Zheng","sequence":"additional","affiliation":[{"name":"Hangzhou City University, Hangzhou, China and Zhejiang University, Hangzhou, China"}]}],"member":"320","published-online":{"date-parts":[[2025,4,22]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"International Conference on Learning Representations.","author":"Brody Shaked","year":"2021","unstructured":"Shaked Brody, Uri Alon, and Eran Yahav. 2021. How Attentive are Graph Attention Networks?. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_2_1","volume-title":"A dendrite method for cluster analysis. Communications in Statistics-theory and Methods","author":"Cali'nski Tadeusz","year":"1974","unstructured":"Tadeusz Cali'nski and Jerzy Harabasz. 1974. A dendrite method for cluster analysis. Communications in Statistics-theory and Methods (1974), 1--27."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.14778\/2824032.2824077"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.1979.4766909"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313488"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3645551"},{"key":"e_1_3_2_1_7_1","volume-title":"International Joint Conference on Artificial Intelligence.","author":"Gao Yang","year":"2021","unstructured":"Yang Gao, Hong Yang, Peng Zhang, Chuan Zhou, and Yue Hu. 2021. Graph neural architecture search. In International Joint Conference on Artificial Intelligence."},{"key":"e_1_3_2_1_8_1","unstructured":"William L. Hamilton Zhitao Ying and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. In Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_1_9_1","unstructured":"Weihua Hu Matthias Fey Marinka Zitnik Yuxiao Dong Hongyu Ren Bowen Liu Michele Catasta and Jure Leskovec. 2020. Open graph benchmark: Datasets for machine learning on graphs. In Advances in Neural Information Processing Systems. 22118--22133."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539429"},{"key":"e_1_3_2_1_11_1","volume-title":"Graph Condensation for Graph Neural Networks. In International Conference on Learning Representations.","author":"Jin Wei","year":"2022","unstructured":"Wei Jin, Lingxiao Zhao, Shichang Zhang, Yozen Liu, Jiliang Tang, and Neil Shah. 2022b. Graph Condensation for Graph Neural Networks. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01545"},{"key":"e_1_3_2_1_13_1","volume-title":"Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations.","author":"Thomas","unstructured":"Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_14_1","volume-title":"Graph condensation via receptive field distribution matching. arXiv preprint arXiv:2206.13697","author":"Liu Mengyang","year":"2022","unstructured":"Mengyang Liu, Shanchuan Li, Xinshi Chen, and Le Song. 2022. Graph condensation via receptive field distribution matching. arXiv preprint arXiv:2206.13697 (2022)."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRS.2023.3298007"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2024.3399936"},{"key":"e_1_3_2_1_17_1","volume-title":"International Conference on Machine Learning.","author":"Liu Yang","year":"2024","unstructured":"Yang Liu, Deyu Bo, and Chuan Shi. 2024a. Graph condensation via eigenbasis matching. In International Conference on Machine Learning."},{"key":"e_1_3_2_1_18_1","volume-title":"International Conference on Learning Representations.","author":"Nguyen Timothy","year":"2020","unstructured":"Timothy Nguyen, Zhourong Chen, and Jaehoon Lee. 2020. Dataset Meta-Learning from Kernel Ridge-Regression. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_19_1","unstructured":"Timothy Nguyen Roman Novak Lechao Xiao and Jaehoon Lee. 2021. Dataset distillation with infinitely wide convolutional networks. 5186--5198."},{"key":"e_1_3_2_1_20_1","volume-title":"Nas-bench-graph: Benchmarking graph neural architecture search. In Advances in Neural Information Processing Systems. 54--69.","author":"Qin Yijian","year":"2022","unstructured":"Yijian Qin, Ziwei Zhang, Xin Wang, Zeyang Zhang, and Wenwu Zhu. 2022. Nas-bench-graph: Benchmarking graph neural architecture search. In Advances in Neural Information Processing Systems. 54--69."},{"key":"e_1_3_2_1_21_1","volume-title":"Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of computational and applied mathematics","author":"Rousseeuw Peter J","year":"1987","unstructured":"Peter J Rousseeuw. 1987. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of computational and applied mathematics (1987), 53--65."},{"key":"e_1_3_2_1_22_1","volume-title":"Active learning for convolutional neural networks: A core-set approach. arXiv preprint arXiv:1708.00489","author":"Sener Ozan","year":"2017","unstructured":"Ozan Sener and Silvio Savarese. 2017. Active learning for convolutional neural networks: A core-set approach. arXiv preprint arXiv:1708.00489 (2017)."},{"key":"e_1_3_2_1_23_1","volume-title":"International Conference on Machine Learning. 9206--9216","author":"Such Felipe Petroski","year":"2020","unstructured":"Felipe Petroski Such, Aditya Rawal, Joel Lehman, Kenneth Stanley, and Jeffrey Clune. 2020. Generative teaching networks: Accelerating neural architecture search by learning to generate synthetic training data. In International Conference on Machine Learning. 9206--9216."},{"key":"e_1_3_2_1_24_1","volume-title":"Graph neural networks: Methods, applications, and opportunities. arXiv preprint arXiv:2108.10733","author":"Waikhom Lilapati","year":"2021","unstructured":"Lilapati Waikhom and Ripon Patgiri. 2021. Graph neural networks: Methods, applications, and opportunities. arXiv preprint arXiv:2108.10733 (2021)."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01188"},{"key":"e_1_3_2_1_26_1","volume-title":"Dataset distillation. arXiv preprint arXiv:1811.10959","author":"Wang Tongzhou","year":"2018","unstructured":"Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, and Alexei A Efros. 2018. Dataset distillation. arXiv preprint arXiv:1811.10959 (2018)."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2024.102404"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671838"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2024.3409071"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553517"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"e_1_3_2_1_32_1","volume-title":"Rumor detection based on propagation graph neural network with attention mechanism. Expert Systems with Applications","author":"Wu Zhiyuan","year":"2020","unstructured":"Zhiyuan Wu, Dechang Pi, Junfu Chen, Meng Xie, and Jianjun Cao. 2020b. Rumor detection based on propagation graph neural network with attention mechanism. Expert Systems with Applications (2020), 113595."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-70344-7_4"},{"key":"e_1_3_2_1_34_1","volume-title":"International Conference on Learning Representations.","author":"Xu Keyulu","year":"2018","unstructured":"Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2018a. How Powerful are Graph Neural Networks?. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_35_1","volume-title":"International Conference on Machine Learning. 5453--5462","author":"Xu Keyulu","year":"2018","unstructured":"Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, and Stefanie Jegelka. 2018b. Representation learning on graphs with jumping knowledge networks. In International Conference on Machine Learning. 5453--5462."},{"key":"e_1_3_2_1_36_1","unstructured":"Beining Yang Kai Wang Qingyun Sun Cheng Ji Xingcheng Fu Hao Tang Yang You and Jianxin Li. 2023. Does graph distillation see like vision dataset counterpart?. In Advances in Neural Information Processing Systems. 53201--53226."},{"key":"e_1_3_2_1_37_1","unstructured":"Chengxuan Ying Tianle Cai Shengjie Luo Shuxin Zheng Guolin Ke Di He Yanming Shen and Tie-Yan Liu. 2021. Do transformers really perform badly for graph representation?. In Advances in Neural Information Processing Systems. 28877--28888."},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219890"},{"key":"e_1_3_2_1_39_1","unstructured":"Zhitao Ying Jiaxuan You Christopher Morris Xiang Ren Will Hamilton and Jure Leskovec. 2018b. Hierarchical graph representation learning with differentiable pooling. In Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_1_40_1","volume-title":"GraphSAINT: Graph Sampling Based Inductive Learning Method. In International Conference on Learning Representations.","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. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_41_1","volume-title":"Language Models-enhanced Semantic Topology Representation Learning For Temporal Knowledge Graph Extrapolation. In ACM International Conference on Information and Knowledge Management. 3227--3236","author":"Zhang Tianli","year":"2024","unstructured":"Tianli Zhang, Tongya Zheng, Zhenbang Xiao, Zulong Chen, Liangyue Li, Zunlei Feng, Dongxiang Zhang, and Mingli Song. 2024b. Language Models-enhanced Semantic Topology Representation Learning For Temporal Knowledge Graph Extrapolation. In ACM International Conference on Information and Knowledge Management. 3227--3236."},{"key":"e_1_3_2_1_42_1","volume-title":"International Conference on Machine Learning.","author":"Zhang Yuchen","year":"2024","unstructured":"Yuchen Zhang, Tianle Zhang, Kai Wang, Ziyao Guo, Yuxuan Liang, Xavier Bresson, Wei Jin, and Yang You. 2024a. Navigating complexity: Toward lossless graph condensation via expanding window matching. In International Conference on Machine Learning."},{"key":"e_1_3_2_1_43_1","volume-title":"International Conference on Machine Learning. 12674--12685","author":"Zhao Bo","year":"2021","unstructured":"Bo Zhao and Hakan Bilen. 2021. Dataset condensation with differentiable siamese augmentation. In International Conference on Machine Learning. 12674--12685."},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/WACV56688.2023.00645"},{"key":"e_1_3_2_1_45_1","volume-title":"Dataset Condensation with Gradient Matching. In International Conference on Learning Representations.","author":"Zhao Bo","year":"2021","unstructured":"Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen. 2021. Dataset Condensation with Gradient Matching. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3220548"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2023.3265271"},{"key":"e_1_3_2_1_48_1","volume-title":"Xingquan Zhu, and Shirui Pan.","author":"Zheng Xin","year":"2024","unstructured":"Xin Zheng, Miao Zhang, Chunyang Chen, Quoc Viet Hung Nguyen, Xingquan Zhu, and Shirui Pan. 2024. Structure-free graph condensation: From large-scale graphs to condensed graph-free data. In Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.14778\/3529337.3529342"}],"event":{"name":"WWW '25: The ACM Web Conference 2025","location":"Sydney NSW Australia","acronym":"WWW '25","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"]},"container-title":["Proceedings of the ACM on Web Conference 2025"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3696410.3714851","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3696410.3714851","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:18:42Z","timestamp":1750295922000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3696410.3714851"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,22]]},"references-count":49,"alternative-id":["10.1145\/3696410.3714851","10.1145\/3696410"],"URL":"https:\/\/doi.org\/10.1145\/3696410.3714851","relation":{},"subject":[],"published":{"date-parts":[[2025,4,22]]},"assertion":[{"value":"2025-04-22","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}