{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T03:03:25Z","timestamp":1792811005411,"version":"4.5.0"},"publisher-location":"New York, NY, USA","reference-count":63,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,4,30]],"date-time":"2023-04-30T00:00:00Z","timestamp":1682812800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Key R&D Program of China","award":["2021YFB1714800"],"award-info":[{"award-number":["2021YFB1714800"]}]},{"DOI":"10.13039\/501100004826","name":"Natural Science Foundation of Beijing Municipality","doi-asserted-by":"publisher","award":["4222030"],"award-info":[{"award-number":["4222030"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Science Foundation of China","award":["62002007"],"award-info":[{"award-number":["62002007"]}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["III-1763325, III-1909323, III-2106758, and SaTC-1930941"],"award-info":[{"award-number":["III-1763325, III-1909323, III-2106758, and SaTC-1930941"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"name":"S&T Program of Hebei","award":["20310101D"],"award-info":[{"award-number":["20310101D"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,4,30]]},"DOI":"10.1145\/3543507.3583453","type":"proceedings-article","created":{"date-parts":[[2023,4,26]],"date-time":"2023-04-26T19:30:25Z","timestamp":1682537425000},"page":"499-510","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":52,"title":["SE-GSL: A General and Effective Graph Structure Learning Framework through Structural Entropy Optimization"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0634-3720","authenticated-orcid":false,"given":"Dongcheng","family":"Zou","sequence":"first","affiliation":[{"name":"Beihang University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0458-5977","authenticated-orcid":false,"given":"Hao","family":"Peng","sequence":"additional","affiliation":[{"name":"Beihang University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3425-5629","authenticated-orcid":false,"given":"Xiang","family":"Huang","sequence":"additional","affiliation":[{"name":"Beihang University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6334-4925","authenticated-orcid":false,"given":"Renyu","family":"Yang","sequence":"additional","affiliation":[{"name":"Beihang University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5152-0055","authenticated-orcid":false,"given":"Jianxin","family":"Li","sequence":"additional","affiliation":[{"name":"Beihang University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1371-5801","authenticated-orcid":false,"given":"Jia","family":"Wu","sequence":"additional","affiliation":[{"name":"Macquarie University, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7282-0284","authenticated-orcid":false,"given":"Chunyang","family":"Liu","sequence":"additional","affiliation":[{"name":"Didi Chuxing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3491-5968","authenticated-orcid":false,"given":"Philip S.","family":"Yu","sequence":"additional","affiliation":[{"name":"University of Illinois Chicago, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,4,30]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"international conference on machine learning. PMLR, 21\u201329","author":"Abu-El-Haija Sami","year":"2019","unstructured":"Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver\u00a0Steeg, and Aram Galstyan. 2019. Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing. In international conference on machine learning. PMLR, 21\u201329."},{"key":"e_1_3_2_1_2_1","volume-title":"Proceedings of the International Conference on Machine Learning. PMLR, 874\u2013883","author":"Bianchi Filippo\u00a0Maria","year":"2020","unstructured":"Filippo\u00a0Maria Bianchi, Daniele Grattarola, and Cesare Alippi. 2020. Spectral clustering with graph neural networks for graph pooling. In Proceedings of the International Conference on Machine Learning. PMLR, 874\u2013883."},{"key":"e_1_3_2_1_3_1","volume-title":"Certifiable robustness to graph perturbations. Advances in Neural Information Processing Systems 32","author":"Bojchevski Aleksandar","year":"2019","unstructured":"Aleksandar Bojchevski and Stephan G\u00fcnnemann. 2019. Certifiable robustness to graph perturbations. Advances in Neural Information Processing Systems 32 (2019)."},{"key":"e_1_3_2_1_4_1","volume-title":"Proceedings of the International Conference on Machine Learning. PMLR, 1725\u20131735","author":"Chen Ming","year":"2020","unstructured":"Ming Chen, Zhewei Wei, Zengfeng Huang, Bolin Ding, and Yaliang Li. 2020. Simple and deep graph convolutional networks. In Proceedings of the International Conference on Machine Learning. PMLR, 1725\u20131735."},{"key":"e_1_3_2_1_5_1","first-page":"19314","article-title":"Iterative deep graph learning for graph neural networks: Better and robust node embeddings","volume":"33","author":"Chen Yu","year":"2020","unstructured":"Yu Chen, Lingfei Wu, and Mohammed Zaki. 2020. Iterative deep graph learning for graph neural networks: Better and robust node embeddings. Advances in Neural Information Processing Systems 33 (2020), 19314\u201319326.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467364"},{"key":"e_1_3_2_1_7_1","first-page":"22092","article-title":"Graph random neural networks for semi-supervised learning on graphs","volume":"33","author":"Feng Wenzheng","year":"2020","unstructured":"Wenzheng Feng, Jie Zhang, Yuxiao Dong, Yu Han, Huanbo Luan, Qian Xu, Qiang Yang, Evgeny Kharlamov, and Jie Tang. 2020. Graph random neural networks for semi-supervised learning on graphs. Advances in Neural Information Processing Systems 33 (2020), 22092\u201322103.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_8_1","volume-title":"Proceedings of the International Conference on Machine Learning. PMLR","author":"Franceschi Luca","year":"2019","unstructured":"Luca Franceschi, Mathias Niepert, Massimiliano Pontil, and Xiao He. 2019. Learning discrete structures for graph neural networks. In Proceedings of the International Conference on Machine Learning. PMLR, 1972\u20131982."},{"key":"e_1_3_2_1_9_1","volume-title":"Proceedings of the International Conference on Learning Representations.","author":"Gasteiger Johannes","year":"2019","unstructured":"Johannes Gasteiger, Aleksandar Bojchevski, and Stephan G\u00fcnnemann. 2019. Predict then Propagate: Graph Neural Networks meet Personalized PageRank. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.5555\/3454287.3455484"},{"key":"e_1_3_2_1_11_1","volume-title":"Advanced methods for knowledge discovery from complex data","author":"Getoor Lise","unstructured":"Lise Getoor. 2005. Link-based classification. In Advanced methods for knowledge discovery from complex data. Springer, 189\u2013207."},{"key":"e_1_3_2_1_12_1","volume-title":"Proceedings of the International Conference on Machine Learning. PMLR, 1263\u20131272","author":"Gilmer Justin","year":"2017","unstructured":"Justin Gilmer, Samuel\u00a0S Schoenholz, Patrick\u00a0F Riley, Oriol Vinyals, and George\u00a0E Dahl. 2017. Neural message passing for quantum chemistry. In Proceedings of the International Conference on Machine Learning. PMLR, 1263\u20131272."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/DSAA.2018.00018"},{"key":"e_1_3_2_1_14_1","volume-title":"Inductive representation learning on large graphs. Advances in Neural Information Processing Systems 30","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 30 (2017)."},{"key":"e_1_3_2_1_15_1","volume-title":"Graphlime: Local interpretable model explanations for graph neural networks","author":"Huang Qiang","year":"2022","unstructured":"Qiang Huang, Makoto Yamada, Yuan Tian, Dinesh Singh, and Yi Chang. 2022. Graphlime: Local interpretable model explanations for graph neural networks. IEEE Transactions on Knowledge and Data Engineering (2022)."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403049"},{"key":"e_1_3_2_1_17_1","volume-title":"Proceedings of the International Conference on Learning Representations.","author":"Keyulu Xu","year":"2019","unstructured":"Xu Keyulu, Hu Weihua, Leskovec Jure, and Stefanie Jegelka. 2019. How powerful are graph neural networks. Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2016.2555904"},{"key":"e_1_3_2_1_19_1","volume-title":"Three-dimensional gene map of cancer cell types: Structural entropy minimisation principle for defining tumour subtypes. Scientific reports 6, 1","author":"Li Angsheng","year":"2016","unstructured":"Angsheng Li, Xianchen Yin, and Yicheng Pan. 2016. Three-dimensional gene map of cancer cell types: Structural entropy minimisation principle for defining tumour subtypes. Scientific reports 6, 1 (2016), 1\u201326."},{"key":"e_1_3_2_1_20_1","volume-title":"Decoding topologically associating domains with ultra-low resolution Hi-C data by graph structural entropy. Nature communications 9, 1","author":"Li Angsheng","year":"2018","unstructured":"Angsheng Li, Xianchen Yin, Bingxiang Xu, Danyang Wang, Jimin Han, Yi Wei, Yun Deng, Ying Xiong, and Zhihua Zhang. 2018. Decoding topologically associating domains with ultra-low resolution Hi-C data by graph structural entropy. Nature communications 9, 1 (2018), 1\u201312."},{"key":"e_1_3_2_1_21_1","volume-title":"Resistance maximization principle for defending networks against virus attack. Physica A: Statistical Mechanics and its Applications 466","author":"Li Angsheng","year":"2017","unstructured":"Angsheng Li, Xiaohui Zhang, and Yicheng Pan. 2017. Resistance maximization principle for defending networks against virus attack. Physica A: Statistical Mechanics and its Applications 466 (2017), 211\u2013223."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539484"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11691"},{"key":"e_1_3_2_1_24_1","volume-title":"REM: From structural entropy to community structure deception. Advances in Neural Information Processing Systems 32","author":"Liu Yiwei","year":"2019","unstructured":"Yiwei Liu, Jiamou Liu, Zijian Zhang, Liehuang Zhu, and Angsheng Li. 2019. REM: From structural entropy to community structure deception. Advances in Neural Information Processing Systems 32 (2019)."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441734"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/381"},{"key":"e_1_3_2_1_27_1","volume-title":"An Information-theoretic Perspective of Hierarchical Clustering. arXiv preprint arXiv:2108.06036","author":"Pan Yicheng","year":"2021","unstructured":"Yicheng Pan, Feng Zheng, and Bingchen Fan. 2021. An Information-theoretic Perspective of Hierarchical Clustering. arXiv preprint arXiv:2108.06036 (2021)."},{"key":"e_1_3_2_1_28_1","volume-title":"Proceedings of the International Conference on Learning Representations.","author":"Pei Hongbin","year":"2019","unstructured":"Hongbin Pei, Bingzhe Wei, Kevin Chen-Chuan Chang, Yu Lei, and Bo Yang. 2019. Geom-GCN: Geometric Graph Convolutional Networks. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/449"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5987"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3490181"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3144993"},{"key":"e_1_3_2_1_33_1","volume-title":"Proceedings of 36th AAAI Conference on Artificial Intelligence.","author":"Qingyun Sun","unstructured":"Sun Qingyun, Li Jianxin, Peng Hao, Wu Jia, Fu Xingcheng, Ji Cheng, and Yu Philip\u00a0S.2022. Graph Structure Learning with Variational Information Bottleneck. In Proceedings of 36th AAAI Conference on Artificial Intelligence."},{"key":"e_1_3_2_1_34_1","volume-title":"Proceedings of the International Conference on Learning Representations.","author":"Rong Yu","year":"2019","unstructured":"Yu Rong, Wenbing Huang, Tingyang Xu, and Junzhou Huang. 2019. DropEdge: Towards Deep Graph Convolutional Networks on Node Classification. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1093\/comnet\/cnab014"},{"key":"e_1_3_2_1_36_1","volume-title":"Twitch gamers: a dataset for evaluating proximity preserving and structural role-based node embeddings. arXiv preprint arXiv:2101.03091","author":"Rozemberczki Benedek","year":"2021","unstructured":"Benedek Rozemberczki and Rik Sarkar. 2021. Twitch gamers: a dataset for evaluating proximity preserving and structural role-based node embeddings. arXiv preprint arXiv:2101.03091 (2021)."},{"key":"e_1_3_2_1_37_1","volume-title":"A mathematical theory of communication. The Bell system technical journal 27, 3","author":"Shannon Claude\u00a0Elwood","year":"1948","unstructured":"Claude\u00a0Elwood Shannon. 1948. A mathematical theory of communication. The Bell system technical journal 27, 3 (1948), 379\u2013423."},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3201243"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557419"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/1557019.1557108"},{"key":"e_1_3_2_1_41_1","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Veli\u010dkovi\u0107 Petar","year":"2017","unstructured":"Petar Veli\u010dkovi\u0107, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. Proceedings of the International Conference on Learning Representations (2017)."},{"key":"e_1_3_2_1_42_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, Jinjing Zhou, Chao Ma, Lingfan Yu, Yu Gai, 2019. 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_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449952"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403177"},{"key":"e_1_3_2_1_45_1","volume-title":"Proceedings of the International Conference on Learning Representations.","author":"Welling Max","year":"2016","unstructured":"Max Welling and Thomas\u00a0N Kipf. 2016. Semi-supervised classification with graph convolutional networks. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/669"},{"key":"e_1_3_2_1_47_1","volume-title":"Proceedings of the International Conference on Machine Learning. PMLR, 24017\u201324030","author":"Wu Junran","year":"2022","unstructured":"Junran Wu, Xueyuan Chen, Ke Xu, and Shangzhe Li. 2022. Structural entropy guided graph hierarchical pooling. In Proceedings of the International Conference on Machine Learning. PMLR, 24017\u201324030."},{"key":"e_1_3_2_1_48_1","first-page":"20437","article-title":"Graph information bottleneck","volume":"33","author":"Wu Tailin","year":"2020","unstructured":"Tailin Wu, Hongyu Ren, Pan Li, and Jure Leskovec. 2020. Graph information bottleneck. Advances in Neural Information Processing Systems 33 (2020), 20437\u201320448.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"e_1_3_2_1_50_1","volume-title":"Proceedings of the International Conference on Learning Representations.","author":"Xu Keyulu","year":"2018","unstructured":"Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2018. How Powerful are Graph Neural Networks?. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_2_1_51_1","volume-title":"Proceedings of the International Conference on Machine Learning. PMLR, 40\u201348","author":"Yang Zhilin","year":"2016","unstructured":"Zhilin Yang, William Cohen, and Ruslan Salakhudinov. 2016. Revisiting semi-supervised learning with graph embeddings. In Proceedings of the International Conference on Machine Learning. PMLR, 40\u201348."},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1145\/3539597.3570467"},{"key":"e_1_3_2_1_53_1","volume-title":"Gnnexplainer: Generating explanations for graph neural networks. Advances in Neural Information Processing Systems 32","author":"Ying Zhitao","year":"2019","unstructured":"Zhitao Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, and Jure Leskovec. 2019. Gnnexplainer: Generating explanations for graph neural networks. Advances in Neural Information Processing Systems 32 (2019)."},{"key":"e_1_3_2_1_54_1","volume-title":"Hierarchical graph representation learning with differentiable pooling. Advances in Neural Information Processing Systems 31","author":"Ying Zhitao","year":"2018","unstructured":"Zhitao Ying, Jiaxuan You, Christopher Morris, Xiang Ren, Will Hamilton, and Jure Leskovec. 2018. Hierarchical graph representation learning with differentiable pooling. Advances in Neural Information Processing Systems 31 (2018)."},{"key":"e_1_3_2_1_55_1","volume-title":"Proceedings of the International Conference on Machine Learning. PMLR, 5708\u20135717","author":"You Jiaxuan","year":"2018","unstructured":"Jiaxuan You, Rex Ying, Xiang Ren, William Hamilton, and Jure Leskovec. 2018. Graphrnn: Generating realistic graphs with deep auto-regressive models. In Proceedings of the International Conference on Machine Learning. PMLR, 5708\u20135717."},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i10.26390"},{"key":"e_1_3_2_1_57_1","first-page":"9263","article-title":"Gnnguard: Defending graph neural networks against adversarial attacks","volume":"33","author":"Zhang Xiang","year":"2020","unstructured":"Xiang Zhang and Marinka Zitnik. 2020. Gnnguard: Defending graph neural networks against adversarial attacks. Advances in Neural Information Processing Systems 33 (2020), 9263\u20139275.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_58_1","volume-title":"Proceedings of the International Conference on Machine Learning. PMLR, 11458\u201311468","author":"Zheng Cheng","year":"2020","unstructured":"Cheng Zheng, Bo Zong, Wei Cheng, Dongjin Song, Jingchao Ni, Wenchao Yu, Haifeng Chen, and Wei Wang. 2020. Robust graph representation learning via neural sparsification. In Proceedings of the International Conference on Machine Learning. PMLR, 11458\u201311468."},{"key":"e_1_3_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.aiopen.2021.01.001"},{"key":"e_1_3_2_1_60_1","first-page":"7793","article-title":"Beyond homophily in graph neural networks: Current limitations and effective designs","volume":"33","author":"Zhu Jiong","year":"2020","unstructured":"Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, and Danai Koutra. 2020. Beyond homophily in graph neural networks: Current limitations and effective designs. Advances in Neural Information Processing Systems 33 (2020), 7793\u20137804.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_61_1","volume-title":"Deep graph structure learning for robust representations: A survey. arXiv preprint arXiv:2103.03036","author":"Zhu Yanqiao","year":"2021","unstructured":"Yanqiao Zhu, Weizhi Xu, Jinghao Zhang, Qiang Liu, Shu Wu, and Liang Wang. 2021. Deep graph structure learning for robust representations: A survey. arXiv preprint arXiv:2103.03036 (2021)."},{"key":"e_1_3_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449802"},{"key":"e_1_3_2_1_63_1","volume-title":"Cagnn: Cluster-aware graph neural networks for unsupervised graph representation learning. arXiv preprint arXiv:2009.01674","author":"Zhu Yanqiao","year":"2020","unstructured":"Yanqiao Zhu, Yichen Xu, Feng Yu, Shu Wu, and Liang Wang. 2020. Cagnn: Cluster-aware graph neural networks for unsupervised graph representation learning. arXiv preprint arXiv:2009.01674 (2020)."}],"event":{"name":"WWW '23: The ACM Web Conference 2023","location":"Austin TX USA","acronym":"WWW '23","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"]},"container-title":["Proceedings of the ACM Web Conference 2023"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3543507.3583453","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/abs\/10.1145\/3543507.3583453","content-type":"text\/html","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3543507.3583453","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3543507.3583453","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T12:47:53Z","timestamp":1750164473000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3543507.3583453"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,30]]},"references-count":63,"alternative-id":["10.1145\/3543507.3583453","10.1145\/3543507"],"URL":"https:\/\/doi.org\/10.1145\/3543507.3583453","relation":{},"subject":[],"published":{"date-parts":[[2023,4,30]]},"assertion":[{"value":"2023-04-30","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}