{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T15:55:37Z","timestamp":1783439737741,"version":"3.54.6"},"publisher-location":"New York, NY, USA","reference-count":47,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,8,14]],"date-time":"2021-08-14T00:00:00Z","timestamp":1628899200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61802069"],"award-info":[{"award-number":["61802069"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shanghai Science and Technology Commission","award":["17JC1420200"],"award-info":[{"award-number":["17JC1420200"]}]},{"name":"Shanghai Sailing Program","award":["18YF1401200"],"award-info":[{"award-number":["18YF1401200"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,8,14]]},"DOI":"10.1145\/3447548.3467256","type":"proceedings-article","created":{"date-parts":[[2021,8,12]],"date-time":"2021-08-12T06:12:09Z","timestamp":1628748729000},"page":"675-684","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":79,"title":["Scaling Up Graph Neural Networks Via Graph Coarsening"],"prefix":"10.1145","author":[{"given":"Zengfeng","family":"Huang","sequence":"first","affiliation":[{"name":"Fudan University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shengzhong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Fudan University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chong","family":"Xi","sequence":"additional","affiliation":[{"name":"Fudan University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tang","family":"Liu","sequence":"additional","affiliation":[{"name":"Fudan University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min","family":"Zhou","sequence":"additional","affiliation":[{"name":"Huawei Technologies Co. Ltd, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,8,14]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"International Conference on Learning Representations.","author":"Bojchevski Aleksandar","year":"2018","unstructured":"Aleksandar Bojchevski and Stephan G\u00fcnnemann . 2018 . Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking . In International Conference on Learning Representations. Aleksandar Bojchevski and Stephan G\u00fcnnemann. 2018. Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403296"},{"key":"e_1_3_2_1_3_1","volume-title":"International Conference on Learning Representations.","author":"Bruna Joan","year":"2014","unstructured":"Joan Bruna , Wojciech Zaremba , Arthur Szlam , and Yann LeCun . 2014 . Spectral networks and locally connected networks on graphs . In International Conference on Learning Representations. Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun. 2014. Spectral networks and locally connected networks on graphs. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_4_1","volume-title":"International Conference on Learning Representations.","author":"Chen Jie","year":"2018","unstructured":"Jie Chen , Tengfei Ma , and Cao Xiao . 2018 a. FastGCN: fast learning with graph convolutional networks via importance sampling . In International Conference on Learning Representations. Jie Chen, Tengfei Ma, and Cao Xiao. 2018a. FastGCN: fast learning with graph convolutional networks via importance sampling. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_5_1","volume-title":"Stochastic Training of Graph Convolutional Networks with Variance Reduction. In International Conference on Machine Learning. 942--950","author":"Chen Jianfei","year":"2018","unstructured":"Jianfei Chen , Jun Zhu , and Le Song . 2018 b. Stochastic Training of Graph Convolutional Networks with Variance Reduction. In International Conference on Machine Learning. 942--950 . Jianfei Chen, Jun Zhu, and Le Song. 2018b. Stochastic Training of Graph Convolutional Networks with Variance Reduction. In International Conference on Machine Learning. 942--950."},{"key":"e_1_3_2_1_6_1","unstructured":"Ming Chen Zhewei Wei Bolin Ding Yaliang Li Ye Yuan Xiaoyong Du and Ji-Rong Wen. 2020 a. Scalable Graph Neural Networks via Bidirectional Propagation. In Advances in Neural Information Processing Systems.  Ming Chen Zhewei Wei Bolin Ding Yaliang Li Ye Yuan Xiaoyong Du and Ji-Rong Wen. 2020 a. Scalable Graph Neural Networks via Bidirectional Propagation. In Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_1_7_1","volume-title":"International Conference on Machine Learning.","author":"Chen Ming","year":"2020","unstructured":"Ming Chen , Zhewei Wei , Zengfeng Huang , Bolin Ding , and Yaliang Li . 2020 b. Simple and deep graph convolutional networks . In International Conference on Machine Learning. Ming Chen, Zhewei Wei, Zengfeng Huang, Bolin Ding, and Yaliang Li. 2020 b. Simple and deep graph convolutional networks. In International Conference on Machine Learning."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330925"},{"key":"e_1_3_2_1_9_1","unstructured":"Fan RK Chung and Fan Chung Graham. 1997. Spectral graph theory. Number 92. American Mathematical Soc.  Fan RK Chung and Fan Chung Graham. 1997. Spectral graph theory. Number 92. American Mathematical Soc."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403192"},{"key":"e_1_3_2_1_11_1","unstructured":"Micha\u00ebl Defferrard Xavier Bresson and Pierre Vandergheynst. 2016. Convolutional neural networks on graphs with fast localized spectral filtering. In Advances in Neural Information Processing Systems. 3844--3852.  Micha\u00ebl Defferrard Xavier Bresson and Pierre Vandergheynst. 2016. Convolutional neural networks on graphs with fast localized spectral filtering. In Advances in Neural Information Processing Systems. 3844--3852."},{"key":"e_1_3_2_1_12_1","volume-title":"GraphZoom: A Multi-level Spectral Approach for Accurate and Scalable Graph Embedding. In International Conference on Learning Representations.","author":"Deng Chenhui","year":"2019","unstructured":"Chenhui Deng , Zhiqiang Zhao , Yongyu Wang , Zhiru Zhang , and Zhuo Feng . 2019 . GraphZoom: A Multi-level Spectral Approach for Accurate and Scalable Graph Embedding. In International Conference on Learning Representations. Chenhui Deng, Zhiqiang Zhao, Yongyu Wang, Zhiru Zhang, and Zhuo Feng. 2019. GraphZoom: A Multi-level Spectral Approach for Accurate and Scalable Graph Embedding. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_13_1","volume-title":"International Conference on Learning Representations.","author":"Dinella Elizabeth","year":"2020","unstructured":"Elizabeth Dinella , Hanjun Dai , Ziyang Li , Mayur Naik , Le Song , and Ke Wang . 2020 . Hoppity: Learning graph transformations to detect and fix bugs in programs . In International Conference on Learning Representations. Elizabeth Dinella, Hanjun Dai, Ziyang Li, Mayur Naik, Le Song, and Ke Wang. 2020. Hoppity: Learning graph transformations to detect and fix bugs in programs. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3179413"},{"key":"e_1_3_2_1_15_1","series-title":"SIAM J. Comput. (2014)","volume-title":"Vertex sparsifiers: New results from old techniques","author":"Englert Matthias","unstructured":"Matthias Englert , Anupam Gupta , Robert Krauthgamer , Harald Racke , Inbal Talgam-Cohen , and Kunal Talwar . 2014. Vertex sparsifiers: New results from old techniques . SIAM J. Comput. (2014) . Matthias Englert, Anupam Gupta, Robert Krauthgamer, Harald Racke, Inbal Talgam-Cohen, and Kunal Talwar. 2014. Vertex sparsifiers: New results from old techniques. SIAM J. Comput. (2014)."},{"key":"e_1_3_2_1_16_1","volume-title":"International Conference on Machine Learning.","author":"Fahrbach Matthew","year":"2020","unstructured":"Matthew Fahrbach , Gramoz Goranci , Richard Peng , Sushant Sachdeva , and Chi Wang . 2020 . Faster graph embeddings via coarsening . In International Conference on Machine Learning. Matthew Fahrbach, Gramoz Goranci, Richard Peng, Sushant Sachdeva, and Chi Wang. 2020. Faster graph embeddings via coarsening. In International Conference on Machine Learning."},{"key":"e_1_3_2_1_17_1","unstructured":"Will Hamilton Zhitao Ying and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Advances in Neural Information Processing Systems. 1024--1034.  Will Hamilton Zhitao Ying and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Advances in Neural Information Processing Systems. 1024--1034."},{"key":"e_1_3_2_1_18_1","volume-title":"International Conference on Artificial Intelligence and Statistics.","author":"Jin Yu","year":"2020","unstructured":"Yu Jin , Andreas Loukas , and Joseph JaJa . 2020 . Graph coarsening with preserved spectral properties . In International Conference on Artificial Intelligence and Statistics. Yu Jin, Andreas Loukas, and Joseph JaJa. 2020. Graph coarsening with preserved spectral properties. In International Conference on Artificial Intelligence and Statistics."},{"key":"e_1_3_2_1_19_1","volume-title":"Perturbation theory for linear operators","author":"Kato Tosio","unstructured":"Tosio Kato . 1995. Perturbation theory for linear operators . Springer Science & Business Media . Tosio Kato. 1995. Perturbation theory for linear operators. Springer Science & Business Media."},{"key":"e_1_3_2_1_20_1","volume-title":"International Conference on Learning Representations.","author":"Kipf Thomas N","year":"2017","unstructured":"Thomas N Kipf and Max Welling . 2017 . Semi-supervised classification with graph convolutional networks . In International Conference on Learning Representations. 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_21_1","volume-title":"International Conference on Learning Representations.","author":"Klicpera Johannes","year":"2019","unstructured":"Johannes Klicpera , Aleksandar Bojchevski , and Stephan G\u00fcnnemann . 2019 . Predict then Propagate: Graph Neural Networks meet Personalized PageRank . In International Conference on Learning Representations. Johannes Klicpera, Aleksandar Bojchevski, and Stephan G\u00fcnnemann. 2019. Predict then Propagate: Graph Neural Networks meet Personalized PageRank. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1247"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/FOCS.2018.00044"},{"key":"e_1_3_2_1_24_1","volume-title":"Mile: A multi-level framework for scalable graph embedding. arXiv preprint arXiv:1802.09612","author":"Liang Jiongqian","year":"2018","unstructured":"Jiongqian Liang , Saket Gurukar , and Srinivasan Parthasarathy . 2018 . Mile: A multi-level framework for scalable graph embedding. arXiv preprint arXiv:1802.09612 (2018). Jiongqian Liang, Saket Gurukar, and Srinivasan Parthasarathy. 2018. Mile: A multi-level framework for scalable graph embedding. arXiv preprint arXiv:1802.09612 (2018)."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403076"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1982.1056489"},{"key":"e_1_3_2_1_27_1","first-page":"1","article-title":"Graph Reduction with Spectral and Cut Guarantees","volume":"20","author":"Loukas Andreas","year":"2019","unstructured":"Andreas Loukas . 2019 . Graph Reduction with Spectral and Cut Guarantees . Journal of Machine Learning Research , Vol. 20 , 116 (2019), 1 -- 42 . Andreas Loukas. 2019. Graph Reduction with Spectral and Cut Guarantees. Journal of Machine Learning Research, Vol. 20, 116 (2019), 1--42.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_1_28_1","volume-title":"Fast Graph Representation Learning with PyTorch Geometric. In International Conference on Learning Representations Workshop .","author":"Jan","unstructured":"Jan E. Lenssen Matthias Fey. 2019 . Fast Graph Representation Learning with PyTorch Geometric. In International Conference on Learning Representations Workshop . Jan E. Lenssen Matthias Fey. 2019. Fast Graph Representation Learning with PyTorch Geometric. In International Conference on Learning Representations Workshop ."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/FOCS.2009.28"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.576"},{"key":"e_1_3_2_1_31_1","volume-title":"International Conference on Learning Representations.","author":"Paliwal Aditya","year":"2020","unstructured":"Aditya Paliwal , Felix Gimeno , Vinod Nair , Yujia Li , Miles Lubin , Pushmeet Kohli , and Oriol Vinyals . 2020 . Reinforced genetic algorithm learning for optimizing computation graphs . In International Conference on Learning Representations. Aditya Paliwal, Felix Gimeno, Vinod Nair, Yujia Li, Miles Lubin, Pushmeet Kohli, and Oriol Vinyals. 2020. Reinforced genetic algorithm learning for optimizing computation graphs. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_32_1","volume-title":"Learning Mesh-Based Simulation with Graph Networks. In International Conference on Learning Representations.","author":"Pfaff Tobias","year":"2021","unstructured":"Tobias Pfaff , Meire Fortunato , Alvaro Sanchez-Gonzalez , and Peter W Battaglia . 2021 . Learning Mesh-Based Simulation with Graph Networks. In International Conference on Learning Representations. Tobias Pfaff, Meire Fortunato, Alvaro Sanchez-Gonzalez, and Peter W Battaglia. 2021. Learning Mesh-Based Simulation with Graph Networks. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_33_1","unstructured":"Morteza Ramezani Weilin Cong Mehrdad Mahdavi Anand Sivasubramaniam and Mahmut Kandemir. 2020. GCN meets GPU: Decoupling \"When to Sample\" from \"How to Sample\". In Advances in Neural Information Processing Systems.  Morteza Ramezani Weilin Cong Mehrdad Mahdavi Anand Sivasubramaniam and Mahmut Kandemir. 2020. GCN meets GPU: Decoupling \"When to Sample\" from \"How to Sample\". In Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_1_34_1","volume-title":"DropEdge: Towards Deep Graph Convolutional Networks on Node Classification. In 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 International Conference on Learning Representations. Yu Rong, Wenbing Huang, Tingyang Xu, and Junzhou Huang. 2019. DropEdge: Towards Deep Graph Convolutional Networks on Node Classification. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_35_1","volume-title":"Sign: Scalable inception graph neural networks. arXiv preprint arXiv:2004.11198","author":"Rossi Emanuele","year":"2020","unstructured":"Emanuele Rossi , Fabrizio Frasca , Ben Chamberlain , Davide Eynard , Michael Bronstein , and Federico Monti . 2020 . Sign: Scalable inception graph neural networks. arXiv preprint arXiv:2004.11198 (2020). Emanuele Rossi, Fabrizio Frasca, Ben Chamberlain, Davide Eynard, Michael Bronstein, and Federico Monti. 2020. Sign: Scalable inception graph neural networks. arXiv preprint arXiv:2004.11198 (2020)."},{"key":"e_1_3_2_1_36_1","volume-title":"Pitfalls of Graph Neural Network Evaluation. arXiv preprint arXiv:1811.05868","author":"Shchur Oleksandr","year":"2018","unstructured":"Oleksandr Shchur , Maximilian Mumme , Aleksandar Bojchevski , and Stephan G\u00fc nnemann. 2018. Pitfalls of Graph Neural Network Evaluation. arXiv preprint arXiv:1811.05868 ( 2018 ). Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, and Stephan G\u00fc nnemann. 2018. Pitfalls of Graph Neural Network Evaluation. arXiv preprint arXiv:1811.05868 (2018)."},{"key":"e_1_3_2_1_37_1","volume-title":"International Conference on Learning Representations.","author":"Velickovic Petar","year":"2018","unstructured":"Petar Velickovic , Guillem Cucurull , Arantxa Casanova , Adriana Romero , Pietro Lio , and Yoshua Bengio . 2018 . Graph attention networks . In International Conference on Learning Representations. Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2018. Graph attention networks. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_38_1","volume-title":"International Conference on Learning Representations.","author":"Wei Jiayi","year":"2020","unstructured":"Jiayi Wei , Maruth Goyal , Greg Durrett , and Isil Dillig . 2020 . Lambdanet: Probabilistic type inference using graph neural networks . In International Conference on Learning Representations. Jiayi Wei, Maruth Goyal, Greg Durrett, and Isil Dillig. 2020. Lambdanet: Probabilistic type inference using graph neural networks. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_39_1","volume-title":"Simplifying Graph Convolutional Networks. In International Conference on Machine Learning. 6861--6871","author":"Wu Felix","year":"2019","unstructured":"Felix Wu , Amauri Souza , Tianyi Zhang , Christopher Fifty , Tao Yu , and Kilian Weinberger . 2019 . Simplifying Graph Convolutional Networks. In International Conference on Machine Learning. 6861--6871 . Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger. 2019. Simplifying Graph Convolutional Networks. In International Conference on Machine Learning. 6861--6871."},{"key":"e_1_3_2_1_40_1","volume-title":"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 International Conference on Learning Representations. Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2018. How Powerful are Graph Neural Networks?. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_41_1","volume-title":"Revisiting Semi-Supervised Learning with Graph Embeddings. In International Conference on Machine Learning. 40--48","author":"Yang Zhilin","year":"2016","unstructured":"Zhilin Yang , William Cohen , and Ruslan Salakhudinov . 2016 . Revisiting Semi-Supervised Learning with Graph Embeddings. In International Conference on Machine Learning. 40--48 . Zhilin Yang, William Cohen, and Ruslan Salakhudinov. 2016. Revisiting Semi-Supervised Learning with Graph Embeddings. In International Conference on Machine Learning. 40--48."},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219890"},{"key":"e_1_3_2_1_43_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. 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_44_1","volume-title":"Inductive Matrix Completion Based on Graph Neural Networks. In International Conference on Learning Representations.","author":"Zhang Muhan","year":"2019","unstructured":"Muhan Zhang and Yixin Chen . 2019 . Inductive Matrix Completion Based on Graph Neural Networks. In International Conference on Learning Representations. Muhan Zhang and Yixin Chen. 2019. Inductive Matrix Completion Based on Graph Neural Networks. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_45_1","volume-title":"Jason Weston, and Bernhard Sch\u00f6lkopf.","author":"Zhou Dengyong","year":"2004","unstructured":"Dengyong Zhou , Olivier Bousquet , Thomas Navin Lal , Jason Weston, and Bernhard Sch\u00f6lkopf. 2004 . Learning with local and global consistency. In Advances in neural information processing systems. Dengyong Zhou, Olivier Bousquet, Thomas Navin Lal, Jason Weston, and Bernhard Sch\u00f6lkopf. 2004. Learning with local and global consistency. In Advances in neural information processing systems."},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449953"},{"key":"e_1_3_2_1_47_1","unstructured":"Difan Zou Ziniu Hu Yewen Wang Song Jiang Yizhou Sun and Quanquan Gu. 2019. Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks. In Advances in neural information processing systems.  Difan Zou Ziniu Hu Yewen Wang Song Jiang Yizhou Sun and Quanquan Gu. 2019. Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks. In Advances in neural information processing systems."}],"event":{"name":"KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Virtual Event Singapore","acronym":"KDD '21","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 27th ACM SIGKDD Conference on Knowledge Discovery &amp; Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3447548.3467256","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3447548.3467256","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:18:28Z","timestamp":1750191508000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3447548.3467256"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,14]]},"references-count":47,"alternative-id":["10.1145\/3447548.3467256","10.1145\/3447548"],"URL":"https:\/\/doi.org\/10.1145\/3447548.3467256","relation":{},"subject":[],"published":{"date-parts":[[2021,8,14]]},"assertion":[{"value":"2021-08-14","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}