{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,12]],"date-time":"2026-04-12T03:40:13Z","timestamp":1775965213520,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":49,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,7,25]],"date-time":"2020-07-25T00:00:00Z","timestamp":1595635200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["IIS1907704"],"award-info":[{"award-number":["IIS1907704"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,7,25]]},"DOI":"10.1145\/3397271.3401092","type":"proceedings-article","created":{"date-parts":[[2020,7,25]],"date-time":"2020-07-25T07:50:08Z","timestamp":1595663408000},"page":"719-728","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":160,"title":["Streaming Graph Neural Networks"],"prefix":"10.1145","author":[{"given":"Yao","family":"Ma","sequence":"first","affiliation":[{"name":"Michigan State University, East Lansing, MI, USA"}]},{"given":"Ziyi","family":"Guo","sequence":"additional","affiliation":[{"name":"JD.com, Beijing, China"}]},{"given":"Zhaocun","family":"Ren","sequence":"additional","affiliation":[{"name":"Shangdong University, Qingdao, China"}]},{"given":"Jiliang","family":"Tang","sequence":"additional","affiliation":[{"name":"Michigan State University, East Lansing, MI, USA"}]},{"given":"Dawei","family":"Yin","sequence":"additional","affiliation":[{"name":"Baidu, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2020,7,25]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Danilo Jimenez Rezende, et al","author":"Battaglia Peter","year":"2016","unstructured":"Peter Battaglia , Razvan Pascanu , Matthew Lai , Danilo Jimenez Rezende, et al . 2016 . Interaction networks for learning about objects, relations and physics. In NIPS. 4502--4510. Peter Battaglia, Razvan Pascanu, Matthew Lai, Danilo Jimenez Rezende, et al. 2016. Interaction networks for learning about objects, relations and physics. In NIPS. 4502--4510."},{"key":"e_1_3_2_1_2_1","unstructured":"Peter W Battaglia Jessica B Hamrick Victor Bapst Alvaro Sanchez-Gonzalez Vinicius Zambaldi Mateusz Malinowski Andrea Tacchetti David Raposo Adam Santoro Ryan Faulkner etal 2018. Relational inductive biases deep learning and graph networks. arXiv preprint arXiv:1806.01261 (2018).  Peter W Battaglia Jessica B Hamrick Victor Bapst Alvaro Sanchez-Gonzalez Vinicius Zambaldi Mateusz Malinowski Andrea Tacchetti David Raposo Adam Santoro Ryan Faulkner et al. 2018. Relational inductive biases deep learning and graph networks. arXiv preprint arXiv:1806.01261 (2018)."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"crossref","unstructured":"Inci M Baytas Cao Xiao Xi Zhang Fei Wang Anil K Jain and Jiayu Zhou. 2017. Patient subtyping via time-aware LSTM networks. In KDD. ACM 65--74.  Inci M Baytas Cao Xiao Xi Zhang Fei Wang Anil K Jain and Jiayu Zhou. 2017. Patient subtyping via time-aware LSTM networks. In KDD. ACM 65--74.","DOI":"10.1145\/3097983.3097997"},{"key":"e_1_3_2_1_4_1","volume-title":"Graph convolutional matrix completion. arXiv preprint arXiv:1706.02263","author":"van den Berg Rianne","year":"2017","unstructured":"Rianne van den Berg , Thomas N Kipf , and Max Welling . 2017. Graph convolutional matrix completion. arXiv preprint arXiv:1706.02263 ( 2017 ). Rianne van den Berg, Thomas N Kipf, and Max Welling. 2017. Graph convolutional matrix completion. arXiv preprint arXiv:1706.02263 (2017)."},{"key":"e_1_3_2_1_5_1","volume-title":"Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203","author":"Bruna Joan","year":"2013","unstructured":"Joan Bruna , Wojciech Zaremba , Arthur Szlam , and Yann LeCun . 2013. Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203 ( 2013 ). Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun. 2013. Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203 (2013)."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1080\/17445760.2012.668546"},{"key":"e_1_3_2_1_7_1","volume-title":"A compositional object-based approach to learning physical dynamics. arXiv preprint arXiv:1612.00341","author":"Chang Michael B","year":"2016","unstructured":"Michael B Chang , Tomer Ullman , Antonio Torralba , and Joshua B Tenenbaum . 2016. A compositional object-based approach to learning physical dynamics. arXiv preprint arXiv:1612.00341 ( 2016 ). Michael B Chang, Tomer Ullman, Antonio Torralba, and Joshua B Tenenbaum. 2016. A compositional object-based approach to learning physical dynamics. arXiv preprint arXiv:1612.00341 (2016)."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"crossref","unstructured":"Shiyu Chang Yang Zhang Jiliang Tang Dawei Yin Yi Chang Mark A HasegawaJohnson and Thomas S Huang. 2017. Streaming recommender systems. In WWW. WWW 381--389.  Shiyu Chang Yang Zhang Jiliang Tang Dawei Yin Yi Chang Mark A HasegawaJohnson and Thomas S Huang. 2017. Streaming recommender systems. In WWW. WWW 381--389.","DOI":"10.1145\/3038912.3052627"},{"key":"e_1_3_2_1_9_1","unstructured":"Micha\u00ebl Defferrard Xavier Bresson and Pierre Vandergheynst. 2016. Convolutional neural networks on graphs with fast localized spectral filtering. In NIPS. 3844--3852.  Micha\u00ebl Defferrard Xavier Bresson and Pierre Vandergheynst. 2016. Convolutional neural networks on graphs with fast localized spectral filtering. In NIPS. 3844--3852."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2018.00113"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/1921632.1921636"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313488"},{"key":"e_1_3_2_1_13_1","volume-title":"Graph u-nets. arXiv preprint arXiv:1905.05178","author":"Gao Hongyang","year":"2019","unstructured":"Hongyang Gao and Shuiwang Ji. 2019. Graph u-nets. arXiv preprint arXiv:1905.05178 ( 2019 ). Hongyang Gao and Shuiwang Ji. 2019. Graph u-nets. arXiv preprint arXiv:1905.05178 (2019)."},{"key":"e_1_3_2_1_14_1","volume-title":"Neural message passing for quantum chemistry. arXiv preprint arXiv:1704.01212","author":"Gilmer Justin","year":"2017","unstructured":"Justin Gilmer , Samuel S Schoenholz , Patrick F Riley , Oriol Vinyals , and George E Dahl . 2017. Neural message passing for quantum chemistry. arXiv preprint arXiv:1704.01212 ( 2017 ). Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl. 2017. Neural message passing for quantum chemistry. arXiv preprint arXiv:1704.01212 (2017)."},{"key":"e_1_3_2_1_15_1","volume-title":"Neural Networks, 2005. IJCNN'05. Proceedings. 2005 IEEE International Joint Conference on","volume":"2","author":"Gori Marco","unstructured":"Marco Gori , Gabriele Monfardini , and Franco Scarselli . [n. d.]. A new model for learning in graph domains . In Neural Networks, 2005. IJCNN'05. Proceedings. 2005 IEEE International Joint Conference on , Vol. 2 . IEEE, 729--734. Marco Gori, Gabriele Monfardini, and Franco Scarselli. [n. d.]. A new model for learning in graph domains. In Neural Networks, 2005. IJCNN'05. Proceedings. 2005 IEEE International Joint Conference on, Vol. 2. IEEE, 729--734."},{"key":"e_1_3_2_1_16_1","volume-title":"DynGEM: Deep Embedding Method for Dynamic Graphs. arXiv preprint arXiv:1805.11273","author":"Goyal Palash","year":"2018","unstructured":"Palash Goyal , Nitin Kamra , Xinran He , and Yan Liu . 2018. DynGEM: Deep Embedding Method for Dynamic Graphs. arXiv preprint arXiv:1805.11273 ( 2018 ). Palash Goyal, Nitin Kamra, Xinran He, and Yan Liu. 2018. DynGEM: Deep Embedding Method for Dynamic Graphs. arXiv preprint arXiv:1805.11273 (2018)."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"crossref","unstructured":"Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. In KDD. ACM 855--864.  Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. In KDD. ACM 855--864.","DOI":"10.1145\/2939672.2939754"},{"key":"e_1_3_2_1_18_1","unstructured":"Will Hamilton Zhitao Ying and Jure Leskovec. 2017. Inductive representation learning on large graphs. In NIPS. 1024--1034.  Will Hamilton Zhitao Ying and Jure Leskovec. 2017. Inductive representation learning on large graphs. In NIPS. 1024--1034."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0895-7177(97)00050-2"},{"key":"e_1_3_2_1_20_1","volume-title":"Long short-term memory. Neural computation 9, 8","author":"Hochreiter Sepp","year":"1997","unstructured":"Sepp Hochreiter and J\u00fcrgen Schmidhuber . 1997. Long short-term memory. Neural computation 9, 8 ( 1997 ), 1735--1780. Sepp Hochreiter and J\u00fcrgen Schmidhuber. 1997. Long short-term memory. Neural computation 9, 8 (1997), 1735--1780."},{"key":"e_1_3_2_1_21_1","volume-title":"Temporal networks. Physics reports 519, 3","author":"Holme Petter","year":"2012","unstructured":"Petter Holme and Jari Saram\u00e4ki . 2012. Temporal networks. Physics reports 519, 3 ( 2012 ), 97--125. Petter Holme and Jari Saram\u00e4ki. 2012. Temporal networks. Physics reports 519, 3 (2012), 97--125."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-017-0533-y"},{"key":"e_1_3_2_1_23_1","volume-title":"Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907","author":"Kipf Thomas N","year":"2016","unstructured":"Thomas N Kipf and Max Welling . 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 ( 2016 ). Thomas N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"crossref","unstructured":"J\u00e9r\u00f4me Kunegis. 2013. Konect: the koblenz network collection. In WWW. ACM 1343--1350.  J\u00e9r\u00f4me Kunegis. 2013. Konect: the koblenz network collection. In WWW. ACM 1343--1350.","DOI":"10.1145\/2487788.2488173"},{"key":"e_1_3_2_1_25_1","unstructured":"Jundong Li Kewei Cheng Liang Wu and Huan Liu. 2018. Streaming link prediction on dynamic attributed networks. In WSDM. ACM 369--377.  Jundong Li Kewei Cheng Liang Wu and Huan Liu. 2018. Streaming link prediction on dynamic attributed networks. In WSDM. ACM 369--377."},{"key":"e_1_3_2_1_26_1","unstructured":"Jundong Li Harsh Dani Xia Hu Jiliang Tang Yi Chang and Huan Liu. 2017. Attributed network embedding for learning in a dynamic environment. In CIKM. ACM 387--396.  Jundong Li Harsh Dani Xia Hu Jiliang Tang Yi Chang and Huan Liu. 2017. Attributed network embedding for learning in a dynamic environment. In CIKM. ACM 387--396."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"crossref","unstructured":"Yu-Ru Lin Yun Chi Shenghuo Zhu Hari Sundaram and Belle L Tseng. 2008. Facetnet: a framework for analyzing communities and their evolutions in dynamic networks. In WWW. ACM 685--694.  Yu-Ru Lin Yun Chi Shenghuo Zhu Hari Sundaram and Belle L Tseng. 2008. Facetnet: a framework for analyzing communities and their evolutions in dynamic networks. In WWW. ACM 685--694.","DOI":"10.1145\/1367497.1367590"},{"key":"e_1_3_2_1_28_1","unstructured":"Jianxin Ma Peng Cui and Wenwu Zhu. 2018. DepthLGP: Learning Embeddings of Out-of-Sample Nodes in Dynamic Networks. AAAI.  Jianxin Ma Peng Cui and Wenwu Zhu. 2018. DepthLGP: Learning Embeddings of Out-of-Sample Nodes in Dynamic Networks. AAAI."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330982"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611975673.74"},{"key":"e_1_3_2_1_31_1","volume-title":"Birds of a feather: Homophily in social networks. Annual review of sociology 27, 1","author":"McPherson Miller","year":"2001","unstructured":"Miller McPherson , Lynn Smith-Lovin , and James M Cook . 2001. Birds of a feather: Homophily in social networks. Annual review of sociology 27, 1 ( 2001 ), 415--444. Miller McPherson, Lynn Smith-Lovin, and James M Cook. 2001. Birds of a feather: Homophily in social networks. Annual review of sociology 27, 1 (2001), 415--444."},{"key":"e_1_3_2_1_32_1","volume-title":"Evolvegcn: Evolving graph convolutional networks for dynamic graphs. arXiv preprint arXiv:1902.10191","author":"Pareja Aldo","year":"2019","unstructured":"Aldo Pareja , Giacomo Domeniconi , Jie Chen , Tengfei Ma , Toyotaro Suzumura , Hiroki Kanezashi , Tim Kaler , and Charles E Leisersen . 2019 . Evolvegcn: Evolving graph convolutional networks for dynamic graphs. arXiv preprint arXiv:1902.10191 (2019). Aldo Pareja, Giacomo Domeniconi, Jie Chen, Tengfei Ma, Toyotaro Suzumura, Hiroki Kanezashi, Tim Kaler, and Charles E Leisersen. 2019. Evolvegcn: Evolving graph convolutional networks for dynamic graphs. arXiv preprint arXiv:1902.10191 (2019)."},{"key":"e_1_3_2_1_33_1","volume-title":"Josh Merel, Martin Riedmiller, Raia Hadsell, and Peter Battaglia.","author":"Sanchez-Gonzalez Alvaro","year":"2018","unstructured":"Alvaro Sanchez-Gonzalez , Nicolas Heess , Jost Tobias Springenberg , Josh Merel, Martin Riedmiller, Raia Hadsell, and Peter Battaglia. 2018 . Graph networks as learnable physics engines for inference and control. arXiv preprint arXiv:1806.01242 (2018). Alvaro Sanchez-Gonzalez, Nicolas Heess, Jost Tobias Springenberg, Josh Merel, Martin Riedmiller, Raia Hadsell, and Peter Battaglia. 2018. Graph networks as learnable physics engines for inference and control. arXiv preprint arXiv:1806.01242 (2018)."},{"key":"e_1_3_2_1_34_1","volume-title":"Dynamic graph representation learning via self-attention networks. arXiv preprint arXiv:1812.09430","author":"Sankar Aravind","year":"2018","unstructured":"Aravind Sankar , Yanhong Wu , Liang Gou , Wei Zhang , and Hao Yang . 2018. Dynamic graph representation learning via self-attention networks. arXiv preprint arXiv:1812.09430 ( 2018 ). Aravind Sankar, Yanhong Wu, Liang Gou, Wei Zhang, and Hao Yang. 2018. Dynamic graph representation learning via self-attention networks. arXiv preprint arXiv:1812.09430 (2018)."},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2008.2005605"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"crossref","unstructured":"J. Tang H. Gao and H. Liu. 2012. mTrust: Discerning multi-faceted trust in a connected world. In WSDM. ACM 93--102.  J. Tang H. Gao and H. Liu. 2012. mTrust: Discerning multi-faceted trust in a connected world. In WSDM. ACM 93--102.","DOI":"10.1145\/2124295.2124309"},{"key":"e_1_3_2_1_37_1","volume-title":"Know-evolve: Deep temporal reasoning for dynamic knowledge graphs. arXiv preprint arXiv:1705.05742","author":"Trivedi Rakshit","year":"2017","unstructured":"Rakshit Trivedi , Hanjun Dai , Yichen Wang , and Le Song . 2017 . Know-evolve: Deep temporal reasoning for dynamic knowledge graphs. arXiv preprint arXiv:1705.05742 (2017). Rakshit Trivedi, Hanjun Dai, Yichen Wang, and Le Song. 2017. Know-evolve: Deep temporal reasoning for dynamic knowledge graphs. arXiv preprint arXiv:1705.05742 (2017)."},{"key":"e_1_3_2_1_38_1","volume-title":"Graph Attention Networks. arXiv preprint arXiv:1710.10903","author":"Veli\u010dkovi\u0107 Petar","year":"2017","unstructured":"Petar Veli\u010dkovi\u0107 , Guillem Cucurull , Arantxa Casanova , Adriana Romero , Pietro Li\u00f2 , and Yoshua Bengio . 2017. Graph Attention Networks. arXiv preprint arXiv:1710.10903 ( 2017 ). Petar Veli\u010dkovi\u0107, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Li\u00f2, and Yoshua Bengio. 2017. Graph Attention Networks. arXiv preprint arXiv:1710.10903 (2017)."},{"key":"e_1_3_2_1_39_1","first-page":"77","article-title":"The TREC-8 Question Answering Track Report","volume":"99","author":"Voorhees Ellen M","year":"1999","unstructured":"Ellen M Voorhees 1999 . The TREC-8 Question Answering Track Report .. In Trec , Vol. 99. 77 -- 82 . Ellen M Voorhees et al. 1999. The TREC-8 Question Answering Track Report.. In Trec, Vol. 99. 77--82.","journal-title":"Trec"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331267"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313562"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380186"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"crossref","unstructured":"Rongjing Xiang Jennifer Neville and Monica Rogati. 2010. Modeling relationship strength in online social networks. In WWW. ACM 981--990.  Rongjing Xiang Jennifer Neville and Monica Rogati. 2010. Modeling relationship strength in online social networks. In WWW. ACM 981--990.","DOI":"10.1145\/1772690.1772790"},{"key":"e_1_3_2_1_44_1","unstructured":"Naganand Yadati Madhav Nimishakavi Prateek Yadav Vikram Nitin Anand Louis and Partha Talukdar. 2019. HyperGCN: A New Method For Training Graph Convolutional Networks on Hypergraphs. In Advances in Neural Information Processing Systems. 1509--1520.  Naganand Yadati Madhav Nimishakavi Prateek Yadav Vikram Nitin Anand Louis and Partha Talukdar. 2019. HyperGCN: A New Method For Training Graph Convolutional Networks on Hypergraphs. In Advances in Neural Information Processing Systems. 1509--1520."},{"key":"e_1_3_2_1_45_1","volume-title":"Graph Convolutional Neural Networks for Web-Scale Recommender Systems. arXiv preprint arXiv:1806.01973","author":"Ying Rex","year":"2018","unstructured":"Rex Ying , Ruining He , Kaifeng Chen , Pong Eksombatchai , William L Hamilton , and Jure Leskovec . 2018. Graph Convolutional Neural Networks for Web-Scale Recommender Systems. arXiv preprint arXiv:1806.01973 ( 2018 ). Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L Hamilton, and Jure Leskovec. 2018. Graph Convolutional Neural Networks for Web-Scale Recommender Systems. arXiv preprint arXiv:1806.01973 (2018)."},{"key":"e_1_3_2_1_46_1","unstructured":"Zhitao Ying Jiaxuan You Christopher Morris Xiang Ren Will Hamilton and Jure Leskovec. 2018. Hierarchical graph representation learning with differentiable pooling. In Advances in neural information processing systems. 4800--4810.  Zhitao Ying Jiaxuan You Christopher Morris Xiang Ren Will Hamilton and Jure Leskovec. 2018. Hierarchical graph representation learning with differentiable pooling. In Advances in neural information processing systems. 4800--4810."},{"key":"e_1_3_2_1_47_1","volume-title":"Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875","author":"Yu Bing","year":"2017","unstructured":"Bing Yu , Haoteng Yin , and Zhanxing Zhu . 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 ( 2017 ). Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017)."},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"crossref","unstructured":"Le-kui Zhou Yang Yang Xiang Ren Fei Wu and Yueting Zhuang. 2018. Dynamic Network Embedding by Modeling Triadic Closure Process.  Le-kui Zhou Yang Yang Xiang Ren Fei Wu and Yueting Zhuang. 2018. Dynamic Network Embedding by Modeling Triadic Closure Process.","DOI":"10.1609\/aaai.v32i1.11257"},{"key":"e_1_3_2_1_49_1","unstructured":"Xiaojin Zhu Zoubin Ghahramani and John D Lafferty. 2003. Semi-supervised learning using gaussian fields and harmonic functions. In ICML. 912--919.  Xiaojin Zhu Zoubin Ghahramani and John D Lafferty. 2003. Semi-supervised learning using gaussian fields and harmonic functions. In ICML. 912--919."}],"event":{"name":"SIGIR '20: The 43rd International ACM SIGIR conference on research and development in Information Retrieval","location":"Virtual Event China","acronym":"SIGIR '20","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3397271.3401092","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3397271.3401092","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3397271.3401092","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T21:31:38Z","timestamp":1750195898000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3397271.3401092"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,25]]},"references-count":49,"alternative-id":["10.1145\/3397271.3401092","10.1145\/3397271"],"URL":"https:\/\/doi.org\/10.1145\/3397271.3401092","relation":{},"subject":[],"published":{"date-parts":[[2020,7,25]]},"assertion":[{"value":"2020-07-25","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}