{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T22:12:36Z","timestamp":1780524756785,"version":"3.54.1"},"publisher-location":"New York, NY, USA","reference-count":48,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,4,19]],"date-time":"2021-04-19T00:00:00Z","timestamp":1618790400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,4,19]]},"DOI":"10.1145\/3442381.3449953","type":"proceedings-article","created":{"date-parts":[[2021,6,3]],"date-time":"2021-06-03T19:35:17Z","timestamp":1622748917000},"page":"1215-1226","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":108,"title":["Interpreting and Unifying Graph Neural Networks with An Optimization Framework"],"prefix":"10.1145","author":[{"given":"Meiqi","family":"Zhu","sequence":"first","affiliation":[{"name":"Beijing University of Posts and Telecommunications, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao","family":"Wang","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuan","family":"Shi","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Houye","family":"Ji","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Cui","sequence":"additional","affiliation":[{"name":"Tsinghua University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,6,3]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Sami Abu-El-Haija Bryan Perozzi Amol Kapoor Nazanin Alipourfard Kristina Lerman Hrayr Harutyunyan Greg\u00a0Ver Steeg and Aram Galstyan. 2019. MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing. In ICML. 21\u201329.  Sami Abu-El-Haija Bryan Perozzi Amol Kapoor Nazanin Alipourfard Kristina Lerman Hrayr Harutyunyan Greg\u00a0Ver Steeg and Aram Galstyan. 2019. MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing. In ICML. 21\u201329."},{"key":"e_1_3_2_1_2_1","unstructured":"Gregor Bachmann Gary Becigneul and Octavian Ganea. 2020. Constant Curvature Graph Convolutional Networks. In ICML. 486\u2013496.  Gregor Bachmann Gary Becigneul and Octavian Ganea. 2020. Constant Curvature Graph Convolutional Networks. In ICML. 486\u2013496."},{"key":"e_1_3_2_1_3_1","unstructured":"Joan Bruna Wojciech Zaremba Arthur Szlam and Yann LeCun. 2014. Spectral Networks and Locally Connected Networks on Graphs. In ICLR.  Joan Bruna Wojciech Zaremba Arthur Szlam and Yann LeCun. 2014. Spectral Networks and Locally Connected Networks on Graphs. In ICLR."},{"key":"e_1_3_2_1_4_1","unstructured":"Ming Chen Zhewei Wei Zengfeng Huang Bolin Ding and Yaliang Li. 2020. Simple and Deep Graph Convolutional Networks. In ICML. 1725\u20131735.  Ming Chen Zhewei Wei Zengfeng Huang Bolin Ding and Yaliang Li. 2020. Simple and Deep Graph Convolutional Networks. In ICML. 1725\u20131735."},{"key":"e_1_3_2_1_5_1","unstructured":"Micha\u00ebl Defferrard Xavier Bresson and Pierre Vandergheynst. 2016. Convolutional neural networks on graphs with fast localized spectral filtering. In NeurIPS. 3844\u20133852.  Micha\u00ebl Defferrard Xavier Bresson and Pierre Vandergheynst. 2016. Convolutional neural networks on graphs with fast localized spectral filtering. In NeurIPS. 3844\u20133852."},{"key":"e_1_3_2_1_6_1","unstructured":"Federico Errica Marco Podda Davide Bacciu and Alessio Micheli. 2020. A Fair Comparison of Graph Neural Networks for Graph Classification. In ICLR.  Federico Errica Marco Podda Davide Bacciu and Alessio Micheli. 2020. A Fair Comparison of Graph Neural Networks for Graph Classification. In ICLR."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"crossref","unstructured":"Wenqi Fan Yao Ma Qing Li Yuan He Eric Zhao Jiliang Tang and Dawei Yin. 2019. Graph Neural Networks for Social Recommendation. In WWW. 417\u2013426.  Wenqi Fan Yao Ma Qing Li Yuan He Eric Zhao Jiliang Tang and Dawei Yin. 2019. Graph Neural Networks for Social Recommendation. In WWW. 417\u2013426.","DOI":"10.1145\/3308558.3313488"},{"key":"e_1_3_2_1_8_1","unstructured":"Hongyang Gao Yongjun Chen and Shuiwang Ji. 2019. Learning Graph Pooling and Hybrid Convolutional Operations for Text Representations. In WWW. 2743\u20132749.  Hongyang Gao Yongjun Chen and Shuiwang Ji. 2019. Learning Graph Pooling and Hybrid Convolutional Operations for Text Representations. In WWW. 2743\u20132749."},{"key":"e_1_3_2_1_9_1","unstructured":"William\u00a0L. Hamilton Rex Ying and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. In NeurIPS. 1024\u20131034.  William\u00a0L. Hamilton Rex Ying and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. In NeurIPS. 1024\u20131034."},{"key":"e_1_3_2_1_10_1","unstructured":"Yifan Hou Jian Zhang James Cheng Kaili Ma Richard T.\u00a0B. Ma Hongzhi Chen and Ming-Chang Yang. 2020. Measuring and Improving the Use of Graph Information in Graph Neural Networks. In ICLR.  Yifan Hou Jian Zhang James Cheng Kaili Ma Richard T.\u00a0B. Ma Hongzhi Chen and Ming-Chang Yang. 2020. Measuring and Improving the Use of Graph Information in Graph Neural Networks. In ICLR."},{"key":"e_1_3_2_1_11_1","unstructured":"Weihua Hu Bowen Liu Joseph Gomes Marinka Zitnik Percy Liang Vijay Pande and Jure Leskovec. 2020. Strategies for Pre-training Graph Neural Networks. In ICLR.  Weihua Hu Bowen Liu Joseph Gomes Marinka Zitnik Percy Liang Vijay Pande and Jure Leskovec. 2020. Strategies for Pre-training Graph Neural Networks. In ICLR."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"crossref","unstructured":"Wei Jin Yao Ma Xiaorui Liu Xianfeng Tang Suhang Wang and Jiliang Tang. 2020. Graph Structure Learning for Robust Graph Neural Networks. In KDD. 66\u201374.  Wei Jin Yao Ma Xiaorui Liu Xianfeng Tang Suhang Wang and Jiliang Tang. 2020. Graph Structure Learning for Robust Graph Neural Networks. In KDD. 66\u201374.","DOI":"10.1145\/3394486.3403049"},{"key":"e_1_3_2_1_13_1","volume-title":"Kipf and Max Welling","author":"N.","year":"2017","unstructured":"Thomas\u00a0 N. Kipf and Max Welling . 2017 . Semi-Supervised Classification with Graph Convolutional Networks. In ICLR. Thomas\u00a0N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In ICLR."},{"key":"e_1_3_2_1_14_1","unstructured":"Johannes Klicpera Aleksandar Bojchevski and Stephan G\u00fcnnemann. 2019. Predict then Propagate: Graph Neural Networks meet Personalized PageRank. In ICLR.  Johannes Klicpera Aleksandar Bojchevski and Stephan G\u00fcnnemann. 2019. Predict then Propagate: Graph Neural Networks meet Personalized PageRank. In ICLR."},{"key":"e_1_3_2_1_15_1","unstructured":"Kwei\u00a0Herng Lai Daochen Zha Kaixiong Zhou and Xia Hu. 2020. PolicyGNN: Aggregation Optimization for Graph Neural Networks. In KDD. 461\u2013471.  Kwei\u00a0Herng Lai Daochen Zha Kaixiong Zhou and Xia Hu. 2020. PolicyGNN: Aggregation Optimization for Graph Neural Networks. In KDD. 461\u2013471."},{"key":"e_1_3_2_1_16_1","unstructured":"Qimai Li Zhichao Han and Xiaoming Wu. 2018. Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning. In AAAI. 3538\u20133545.  Qimai Li Zhichao Han and Xiaoming Wu. 2018. Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning. In AAAI. 3538\u20133545."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"crossref","unstructured":"Meng Liu Hongyang Gao and Shuiwang Ji. 2020. Towards Deeper Graph Neural Networks. In KDD. 338\u2013348.  Meng Liu Hongyang Gao and Shuiwang Ji. 2020. Towards Deeper Graph Neural Networks. In KDD. 338\u2013348.","DOI":"10.1145\/3394486.3403076"},{"key":"e_1_3_2_1_18_1","unstructured":"Andreas Loukas. 2020. What graph neural networks cannot learn: depth vs width. In ICLR.  Andreas Loukas. 2020. What graph neural networks cannot learn: depth vs width. In ICLR."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"crossref","unstructured":"Yao Ma Xiaorui Liu Tong Zhao Yozen Liu Jiliang Tang and Neil Shah. 2020. A Unified View on Graph Neural Networks as Graph Signal Denoising.arXiv preprint arXiv:2010.01777(2020).  Yao Ma Xiaorui Liu Tong Zhao Yozen Liu Jiliang Tang and Neil Shah. 2020. A Unified View on Graph Neural Networks as Graph Signal Denoising.arXiv preprint arXiv:2010.01777(2020).","DOI":"10.1145\/3459637.3482225"},{"key":"e_1_3_2_1_20_1","unstructured":"P\u00e9ter Mernyei and Catalina Cangea. 2020. Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networks. arXiv preprint arXiv:2007.02901(2020).  P\u00e9ter Mernyei and Catalina Cangea. 2020. Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networks. arXiv preprint arXiv:2007.02901(2020)."},{"key":"e_1_3_2_1_21_1","unstructured":"Hoang Nt and Takanori Maehara. 2019. Revisiting Graph Neural Networks: All We Have is Low-Pass Filters. arXiv preprint arXiv:1905.09550(2019).  Hoang Nt and Takanori Maehara. 2019. Revisiting Graph Neural Networks: All We Have is Low-Pass Filters. arXiv preprint arXiv:1905.09550(2019)."},{"key":"e_1_3_2_1_22_1","unstructured":"Kenta Oono and Taiji Suzuki. 2020. Graph Neural Networks Exponentially Lose Expressive Power for Node Classification. In ICLR.  Kenta Oono and Taiji Suzuki. 2020. Graph Neural Networks Exponentially Lose Expressive Power for Node Classification. In ICLR."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/72.159058"},{"key":"e_1_3_2_1_24_1","unstructured":"Adam Paszke Sam Gross Soumith Chintala Gregory Chanan Edward Yang Zachary DeVito Zeming Lin Alban Desmaison Luca Antiga and Adam Lerer. 2017. Automatic differentiation in PyTorch. (2017).  Adam Paszke Sam Gross Soumith Chintala Gregory Chanan Edward Yang Zachary DeVito Zeming Lin Alban Desmaison Luca Antiga and Adam Lerer. 2017. Automatic differentiation in PyTorch. (2017)."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403168"},{"key":"e_1_3_2_1_26_1","unstructured":"Yu Rong Wenbing Huang Tingyang Xu and Junzhou Huang. 2020. DropEdge: Towards Deep Graph Convolutional Networks on Node Classification. In ICLR.  Yu Rong Wenbing Huang Tingyang Xu and Junzhou Huang. 2020. DropEdge: Towards Deep Graph Convolutional Networks on Node Classification. In ICLR."},{"key":"e_1_3_2_1_27_1","unstructured":"Chenyang Si Wentao Chen Wei Wang Liang Wang and Tieniu Tan. 2019. An Attention Enhanced Graph Convolutional LSTM Network for Skeleton-Based Action Recognition. In CVPR. 1227\u20131236.  Chenyang Si Wentao Chen Wei Wang Liang Wang and Tieniu Tan. 2019. An Attention Enhanced Graph Convolutional LSTM Network for Skeleton-Based Action Recognition. In CVPR. 1227\u20131236."},{"key":"e_1_3_2_1_28_1","unstructured":"Qiaoyu Tan Ninghao Liu Xing Zhao Hongxia Yang Jingren Zhou and Xia Hu. 2020. Learning to Hash with Graph Neural Networks for Recommender Systems. In WWW. 1988\u20131998.  Qiaoyu Tan Ninghao Liu Xing Zhao Hongxia Yang Jingren Zhou and Xia Hu. 2020. Learning to Hash with Graph Neural Networks for Recommender Systems. In WWW. 1988\u20131998."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"crossref","unstructured":"Damien Teney Lingqiao Liu and Anton van\u00a0den Hengel. 2017. Graph-Structured Representations for Visual Question Answering. In CVPR. 3233\u20133241.  Damien Teney Lingqiao Liu and Anton van\u00a0den Hengel. 2017. Graph-Structured Representations for Visual Question Answering. In CVPR. 3233\u20133241.","DOI":"10.1109\/CVPR.2017.344"},{"key":"e_1_3_2_1_30_1","unstructured":"Petar Veli\u010dkovi\u0107 Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Li\u00f2 and Yoshua Bengio. 2018. Graph Attention Networks. ICLR.  Petar Veli\u010dkovi\u0107 Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Li\u00f2 and Yoshua Bengio. 2018. Graph Attention Networks. ICLR."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"crossref","unstructured":"Chun Wang Shirui Pan Ruiqi Hu Guodong Long Jing Jiang and Chengqi Zhang. 2019. Attributed Graph Clustering: a Deep Attentional Embedding approach. In IJCAI. 3670\u20133676.  Chun Wang Shirui Pan Ruiqi Hu Guodong Long Jing Jiang and Chengqi Zhang. 2019. Attributed Graph Clustering: a Deep Attentional Embedding approach. In IJCAI. 3670\u20133676.","DOI":"10.24963\/ijcai.2019\/509"},{"key":"e_1_3_2_1_32_1","unstructured":"Minjie Wang Da Zheng Zihao Ye Quan Gan Mufei Li Xiang Song Jinjing Zhou Chao Ma Lingfan Yu Yu Gai Tianjun Xiao Tong He George Karypis Jinyang Li and Zheng Zhang. 2019. Deep Graph Library: A Graph-Centric Highly-Performant Package for Graph Neural Networks. arXiv preprint arXiv:1909.01315(2019).  Minjie Wang Da Zheng Zihao Ye Quan Gan Mufei Li Xiang Song Jinjing Zhou Chao Ma Lingfan Yu Yu Gai Tianjun Xiao Tong He George Karypis Jinyang Li and Zheng Zhang. 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_33_1","doi-asserted-by":"crossref","unstructured":"Xiao Wang Houye Ji Chuan Shi Bai Wang Yanfang Ye Peng Cui and Philip\u00a0S Yu. 2019. Heterogeneous Graph Attention Network. In WWW. 2022\u20132032.  Xiao Wang Houye Ji Chuan Shi Bai Wang Yanfang Ye Peng Cui and Philip\u00a0S Yu. 2019. Heterogeneous Graph Attention Network. In WWW. 2022\u20132032.","DOI":"10.1145\/3308558.3313562"},{"key":"e_1_3_2_1_34_1","unstructured":"Felix Wu Amauri Souza Tianyi Zhang Christopher Fifty Tao Yu and Kilian Weinberger. 2019. Simplifying Graph Convolutional Networks. In ICML. 6861\u20136871.  Felix Wu Amauri Souza Tianyi Zhang Christopher Fifty Tao Yu and Kilian Weinberger. 2019. Simplifying Graph Convolutional Networks. In ICML. 6861\u20136871."},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"e_1_3_2_1_36_1","unstructured":"Bingbing Xu Huawei Shen Qi Cao Yunqi Qiu and Xueqi Cheng. 2019. Graph Wavelet Neural Network. In ICLR.  Bingbing Xu Huawei Shen Qi Cao Yunqi Qiu and Xueqi Cheng. 2019. Graph Wavelet Neural Network. In ICLR."},{"key":"e_1_3_2_1_37_1","unstructured":"Keyulu Xu Weihua Hu Jure Leskovec and Stefanie Jegelka. 2019. How Powerful are Graph Neural Networks. In ICLR.  Keyulu Xu Weihua Hu Jure Leskovec and Stefanie Jegelka. 2019. How Powerful are Graph Neural Networks. In ICLR."},{"key":"e_1_3_2_1_38_1","volume-title":"Ken ichi Kawarabayashi, and Stefanie Jegelka","author":"Xu Keyulu","year":"2018","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. 5449\u20135458. 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. 5449\u20135458."},{"key":"e_1_3_2_1_39_1","unstructured":"Zhilin Yang William\u00a0W. Cohen and Ruslan Salakhutdinov. 2016. Revisiting semi-supervised learning with graph embeddings. In ICML. 40\u201348.  Zhilin Yang William\u00a0W. Cohen and Ruslan Salakhutdinov. 2016. Revisiting semi-supervised learning with graph embeddings. In ICML. 40\u201348."},{"key":"e_1_3_2_1_40_1","unstructured":"Zhitao Ying Dylan Bourgeois Jiaxuan You Marinka Zitnik and Jure Leskovec. 2019. GNNExplainer: Generating Explanations for Graph Neural Networks. In NeurIPS. 9240\u20139251.  Zhitao Ying Dylan Bourgeois Jiaxuan You Marinka Zitnik and Jure Leskovec. 2019. GNNExplainer: Generating Explanations for Graph Neural Networks. In NeurIPS. 9240\u20139251."},{"key":"e_1_3_2_1_41_1","unstructured":"Zhitao Ying Ines Chami Christopher R\u00e9 and Jure Leskovec. 2019. Hyperbolic Graph Convolutional Neural Networks. In NeurIPS. 4869\u20134880.  Zhitao Ying Ines Chami Christopher R\u00e9 and Jure Leskovec. 2019. Hyperbolic Graph Convolutional Neural Networks. In NeurIPS. 4869\u20134880."},{"key":"e_1_3_2_1_42_1","volume-title":"XGNN: Towards Model-Level Explanations of Graph Neural Networks. In KDD. 430\u2013438.","author":"Yuan Hao","year":"2020","unstructured":"Hao Yuan , Jiliang Tang , Xia Hu , and Shuiwang Ji . 2020 . XGNN: Towards Model-Level Explanations of Graph Neural Networks. In KDD. 430\u2013438. Hao Yuan, Jiliang Tang, Xia Hu, and Shuiwang Ji. 2020. XGNN: Towards Model-Level Explanations of Graph Neural Networks. In KDD. 430\u2013438."},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"crossref","unstructured":"Chuxu Zhang Dongjin Song Chao Huang Ananthram Swami and Nitesh\u00a0V. Chawla. 2019. Heterogeneous Graph Neural Network. In KDD. 793\u2013803.  Chuxu Zhang Dongjin Song Chao Huang Ananthram Swami and Nitesh\u00a0V. Chawla. 2019. Heterogeneous Graph Neural Network. In KDD. 793\u2013803.","DOI":"10.1145\/3292500.3330961"},{"key":"e_1_3_2_1_44_1","volume-title":"Deep Learning on Graphs: A Survey","author":"Zhang Ziwei","year":"2020","unstructured":"Ziwei Zhang , Peng Cui , and Wenwu Zhu . 2020. Deep Learning on Graphs: A Survey . IEEE Transactions on Knowledge and Data Engineering ( 2020 ), 1\u20131. Ziwei Zhang, Peng Cui, and Wenwu Zhu. 2020. Deep Learning on Graphs: A Survey. IEEE Transactions on Knowledge and Data Engineering (2020), 1\u20131."},{"key":"e_1_3_2_1_45_1","volume-title":"Graph Neural Networks: A Review of Methods and Applications. arXiv: Learning","author":"Zhou Jie","year":"2018","unstructured":"Jie Zhou , Ganqu Cui , Zhengyan Zhang , Cheng Yang , Zhiyuan Liu , and Maosong Sun . 2018. Graph Neural Networks: A Review of Methods and Applications. arXiv: Learning ( 2018 ). Jie Zhou, Ganqu Cui, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, and Maosong Sun. 2018. Graph Neural Networks: A Review of Methods and Applications. arXiv: Learning (2018)."},{"key":"e_1_3_2_1_46_1","unstructured":"Dingyuan Zhu Ziwei Zhang Peng Cui and Wenwu Zhu. 2019. Robust Graph Convolutional Networks Against Adversarial Attacks. In KDD. 1399\u20131407.  Dingyuan Zhu Ziwei Zhang Peng Cui and Wenwu Zhu. 2019. Robust Graph Convolutional Networks Against Adversarial Attacks. In KDD. 1399\u20131407."},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"crossref","unstructured":"Hao Zhu Yankai Lin Zhiyuan Liu Jie Fu Tat-Seng Chua and Maosong Sun. 2019. Graph Neural Networks with Generated Parameters for Relation Extraction. In ACL. 1331\u20131339.  Hao Zhu Yankai Lin Zhiyuan Liu Jie Fu Tat-Seng Chua and Maosong Sun. 2019. Graph Neural Networks with Generated Parameters for Relation Extraction. In ACL. 1331\u20131339.","DOI":"10.18653\/v1\/P19-1128"},{"key":"e_1_3_2_1_48_1","unstructured":"Xiaojin Zhu John Lafferty and Ronald Rosenfeld. 2005. Semi-supervised learning with graphs. (2005).  Xiaojin Zhu John Lafferty and Ronald Rosenfeld. 2005. Semi-supervised learning with graphs. (2005)."}],"event":{"name":"WWW '21: The Web Conference 2021","location":"Ljubljana Slovenia","acronym":"WWW '21","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"]},"container-title":["Proceedings of the Web Conference 2021"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3442381.3449953","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3442381.3449953","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T21:24:32Z","timestamp":1750195472000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3442381.3449953"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,19]]},"references-count":48,"alternative-id":["10.1145\/3442381.3449953","10.1145\/3442381"],"URL":"https:\/\/doi.org\/10.1145\/3442381.3449953","relation":{},"subject":[],"published":{"date-parts":[[2021,4,19]]},"assertion":[{"value":"2021-06-03","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}