{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T10:51:36Z","timestamp":1778669496440,"version":"3.51.4"},"reference-count":61,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2021,4,21]],"date-time":"2021-04-21T00:00:00Z","timestamp":1618963200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"NSFC","doi-asserted-by":"crossref","award":["61632009"],"award-info":[{"award-number":["61632009"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Guangdong Provincial NSF","award":["2017A030308006"],"award-info":[{"award-number":["2017A030308006"]}]},{"name":"Open Project of Zhejiang Lab","award":["2019KE0AB02"],"award-info":[{"award-number":["2019KE0AB02"]}]},{"DOI":"10.13039\/501100004543","name":"China Scholarships Council","doi-asserted-by":"crossref","award":["201906130131"],"award-info":[{"award-number":["201906130131"]}],"id":[{"id":"10.13039\/501100004543","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Science and Technology Program of Changsha City","award":["kq 2004017"],"award-info":[{"award-number":["kq 2004017"]}]},{"name":"NSF","award":["CNS 1824440, CNS 1828363, CNS 1757533, CNS 1618398, CNS 1651947, and CNS 1564128"],"award-info":[{"award-number":["CNS 1824440, CNS 1828363, CNS 1757533, CNS 1618398, CNS 1651947, and CNS 1564128"]}]},{"name":"National Outstanding Youth Science Program of NSFC","award":["61625202"],"award-info":[{"award-number":["61625202"]}]},{"DOI":"10.13039\/501100001809","name":"NSFC","doi-asserted-by":"crossref","award":["61872127"],"award-info":[{"award-number":["61872127"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2021,6,30]]},"abstract":"<jats:p>CANDECOMP\/PARAFAC (CP) decomposition is widely used in various online social network (OSN) applications. However, it is inefficient when dealing with massive and incremental data. Some incremental CP decomposition (ICP) methods have been proposed to improve the efficiency and process evolving data, by updating decomposition results according to the newly added data. The ICP methods are efficient, but inaccurate because of serious error accumulation caused by approximation in the incremental updating. To promote the wide use of ICP, we strive to reduce its cumulative errors while keeping high efficiency. We first differentiate all possible errors in ICP into two types: the cumulative reconstruction error and the prediction error. Next, we formulate two optimization problems for reducing the two errors. Then, we propose several restarting strategies to address the two problems. Finally, we test the effectiveness in three typical dynamic OSN applications. To the best of our knowledge, this is the first work on reducing the cumulative errors of the ICP methods in dynamic OSNs.<\/jats:p>","DOI":"10.1145\/3441645","type":"journal-article","created":{"date-parts":[[2021,4,21]],"date-time":"2021-04-21T15:42:54Z","timestamp":1619019774000},"page":"1-33","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Reducing Cumulative Errors of Incremental CP Decomposition in Dynamic Online Social Networks"],"prefix":"10.1145","volume":"15","author":[{"given":"Jingjing","family":"Wang","sequence":"first","affiliation":[{"name":"Hunan University, Changsha City, Hunan Province, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4985-7454","authenticated-orcid":false,"given":"Wenjun","family":"Jiang","sequence":"additional","affiliation":[{"name":"Hunan University, Changsha City, Hunan Province, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kenli","family":"Li","sequence":"additional","affiliation":[{"name":"Hunan University, Changsha City, Hunan Province, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Keqin","family":"Li","sequence":"additional","affiliation":[{"name":"Hunan University and State University of New York, New York, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,4,21]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"2018. Behance.net social network. Retrieved from http:\/\/www.behance.net\/.  2018. Behance.net social network. Retrieved from http:\/\/www.behance.net\/."},{"key":"e_1_2_1_2_1","volume-title":"Proceedings of the 2009 IEEE International Conference on Data Mining Workshops. IEEE, 262\u2013269","author":"Acar Evrim","unstructured":"Evrim Acar , Daniel M. Dunlavy , and Tamara G. Kolda . 2009. Link prediction on evolving data using matrix and tensor factorizations . In Proceedings of the 2009 IEEE International Conference on Data Mining Workshops. IEEE, 262\u2013269 . Evrim Acar, Daniel M. Dunlavy, and Tamara G. Kolda. 2009. Link prediction on evolving data using matrix and tensor factorizations. In Proceedings of the 2009 IEEE International Conference on Data Mining Workshops. IEEE, 262\u2013269."},{"key":"e_1_2_1_3_1","volume-title":"Zaki","author":"Hasan Mohammad Al","year":"2011","unstructured":"Mohammad Al Hasan and Mohammed J . Zaki . 2011 . A survey of link prediction in social networks. In Social Network Data Analytics. Springer , 243\u2013275. Mohammad Al Hasan and Mohammed J. Zaki. 2011. A survey of link prediction in social networks. In Social Network Data Analytics. Springer, 243\u2013275."},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/2487788.2487877"},{"key":"e_1_2_1_5_1","first-page":"80","article-title":"Predicting the popularity of online content with group-specific models","volume":"53","author":"Cao Qi","year":"2017","unstructured":"Qi Cao , Huawei Shen , Hao Gao , Jinhua Gao , and Xueqi Cheng . 2017 . Predicting the popularity of online content with group-specific models . Communications of the ACM 53 , 8 (2017), 80 \u2013 88 . Qi Cao, Huawei Shen, Hao Gao, Jinhua Gao, and Xueqi Cheng. 2017. Predicting the popularity of online content with group-specific models. Communications of the ACM 53, 8 (2017), 80\u201388.","journal-title":"Communications of the ACM"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611974010.63"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.4882"},{"key":"e_1_2_1_8_1","volume-title":"Temporal link prediction using matrix and tensor factorizations. ACM Trans Knowledge Discovery from Data 5, 2","author":"Dunlavy Daniel M.","year":"2011","unstructured":"Daniel M. Dunlavy , Tamara G. Kolda , and Evrim Acar . 2011. Temporal link prediction using matrix and tensor factorizations. ACM Trans Knowledge Discovery from Data 5, 2 ( 2011 ), 1\u201327. Daniel M. Dunlavy, Tamara G. Kolda, and Evrim Acar. 2011. Temporal link prediction using matrix and tensor factorizations. ACM Trans Knowledge Discovery from Data 5, 2 (2011), 1\u201327."},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.automatica.2011.03.008"},{"key":"e_1_2_1_10_1","volume-title":"Proceedings of the 32nd AAAI Conference on Artificial Intelligence 32","author":"Zhang Ziwei","year":"2018","unstructured":"Ziwei Zhang , Peng Cui , Jian Pei , XiaoWang, and Wenwu Zhu . 2018 . TIMERS: Error-bounded SVD restart on dynamic networks . Proceedings of the 32nd AAAI Conference on Artificial Intelligence 32 , 1 (2018), 224--231. Ziwei Zhang, Peng Cui, Jian Pei, XiaoWang, and Wenwu Zhu. 2018. TIMERS: Error-bounded SVD restart on dynamic networks. Proceedings of the 32nd AAAI Conference on Artificial Intelligence 32, 1 (2018), 224--231."},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/CIDM.2011.5949306"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/2063576.2063744"},{"key":"e_1_2_1_13_1","volume-title":"Taxonomy and evaluation for microblog popularity prediction. ACM Transactions on Knowledge Discovery from Data 13, 2","author":"Gao Xiaofeng","year":"2019","unstructured":"Xiaofeng Gao , Zhenhao Cao , Sha Li , Bin Yao , Guihai Chen , and Shaojie Tang . 2019. Taxonomy and evaluation for microblog popularity prediction. ACM Transactions on Knowledge Discovery from Data 13, 2 ( 2019 ), 15. Xiaofeng Gao, Zhenhao Cao, Sha Li, Bin Yao, Guihai Chen, and Shaojie Tang. 2019. Taxonomy and evaluation for microblog popularity prediction. ACM Transactions on Knowledge Discovery from Data 13, 2 (2019), 15."},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/2959100.2959151"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1002\/gamm.201310004"},{"key":"e_1_2_1_16_1","volume-title":"Papalexakis","author":"Gujral Ekta","year":"2018","unstructured":"Ekta Gujral , Ravdeep Pasricha , and Evangelos E . Papalexakis . 2018 . Sambaten : Sampling-based batch incremental tensor decomposition. In Proceedings of the 2018 SIAM International Conference on Data Mining. SIAM , 387\u2013395. Ekta Gujral, Ravdeep Pasricha, and Evangelos E. Papalexakis. 2018. Sambaten: Sampling-based batch incremental tensor decomposition. In Proceedings of the 2018 SIAM International Conference on Data Mining. SIAM, 387\u2013395."},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/0196-6774(90)90014-6"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1038\/srep31708"},{"key":"e_1_2_1_19_1","volume-title":"Proceedings of the 26th International Conference on World Wide Web. 725\u2013733","author":"Hoang Minh X.","unstructured":"Minh X. Hoang , Xuan-Hong Dang , Xiang Wu , Zhenyu Yan , and Ambuj K. Singh . 2017. GPOP: Scalable group-level popularity prediction for online content in social networks . In Proceedings of the 26th International Conference on World Wide Web. 725\u2013733 . Minh X. Hoang, Xuan-Hong Dang, Xiang Wu, Zhenyu Yan, and Ambuj K. Singh. 2017. GPOP: Scalable group-level popularity prediction for online content in social networks. In Proceedings of the 26th International Conference on World Wide Web. 725\u2013733."},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3414841"},{"key":"e_1_2_1_21_1","first-page":"1","article-title":"Temporal scaling in information propagation","volume":"4","author":"Huang Junming","year":"2014","unstructured":"Junming Huang , Chao Li , Wen-Qiang Wang , Hua-Wei Shen , Guojie Li , and Xue-Qi Cheng . 2014 . Temporal scaling in information propagation . Scientific Reports 4 , 1 (2014), 1 -- 6 . Junming Huang, Chao Li, Wen-Qiang Wang, Hua-Wei Shen, Guojie Li, and Xue-Qi Cheng. 2014. Temporal scaling in information propagation. Scientific Reports 4, 1 (2014), 1--6.","journal-title":"Scientific Reports"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/2906151"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2015.2444842"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1006\/jpdc.1997.1404"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1137\/07070111X"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553447"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/1772690.1772751"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/1772690.1772754"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1002\/sapm1946251261"},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/2808797.2809355"},{"key":"e_1_2_1_31_1","volume-title":"Proceedings of the 26th International Joint Conference on Artificial Intelligence. 2180\u20132186","author":"Zhang Luming Xu Xianghua Jin Xiaojie","year":"2017","unstructured":"Jin Xiaojie Zhang Luming Xu Xianghua Yan Shuicheng Li Ping , Feng Jiashi . 2017 . Online robust low-rank tensor learning ping . In Proceedings of the 26th International Joint Conference on Artificial Intelligence. 2180\u20132186 . Jin Xiaojie Zhang Luming Xu Xianghua Yan Shuicheng Li Ping, Feng Jiashi. 2017. Online robust low-rank tensor learning ping. In Proceedings of the 26th International Joint Conference on Artificial Intelligence. 2180\u20132186."},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1002\/asi.20591"},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2010.11.027"},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3012704"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-23783-6_28"},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13278-017-0483-9"},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939751"},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/2915921"},{"key":"e_1_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/3018661.3018731"},{"key":"e_1_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.123.128301"},{"key":"e_1_2_1_41_1","volume-title":"Proceedings of the 2013 ACM International Conference on Web Search and Data Mining. ACM, 365\u2013374","author":"Pinto Henrique","unstructured":"Henrique Pinto , Jussara M. Almeida , and Marcos A. Gon\u00e7alves . 2013. Using early view patterns to predict the popularity of Youtube videos . In Proceedings of the 2013 ACM International Conference on Web Search and Data Mining. ACM, 365\u2013374 . Henrique Pinto, Jussara M. Almeida, and Marcos A. Gon\u00e7alves. 2013. Using early view patterns to predict the popularity of Youtube videos. In Proceedings of the 2013 ACM International Conference on Web Search and Data Mining. ACM, 365\u2013374."},{"key":"e_1_2_1_42_1","volume-title":"Control profiles of complex networks. Science 343, 6177","author":"Ruths Justin","year":"2014","unstructured":"Justin Ruths and Derek Ruths . 2014. Control profiles of complex networks. Science 343, 6177 ( 2014 ), 1373\u20131376. Justin Ruths and Derek Ruths. 2014. Control profiles of complex networks. Science 343, 6177 (2014), 1373\u20131376."},{"key":"e_1_2_1_43_1","volume-title":"Patterns and dynamics of users","author":"Swain Nigel","year":"2010","unstructured":"Nigel Swain . 2010. Patterns and dynamics of users \u2019 behavior and interaction: Network analysis of an online community.Journal of the Association for Information Science and Technology 60, 5 ( 2010 ), 911\u2013932. Nigel Swain. 2010. Patterns and dynamics of users\u2019 behavior and interaction: Network analysis of an online community.Journal of the Association for Information Science and Technology 60, 5 (2010), 911\u2013932."},{"key":"e_1_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/1787234.1787254"},{"key":"e_1_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13278-014-0174-8"},{"key":"e_1_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13174-014-0008-y"},{"key":"e_1_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/1988688.1988766"},{"key":"e_1_2_1_48_1","volume-title":"Proceedings of the 7th IEEE International Conference on Data Mining.","author":"Wang Chao","unstructured":"Chao Wang , V. Satuluri , and S. Parthasarathy . 2007. Local probabilistic models for link prediction . In Proceedings of the 7th IEEE International Conference on Data Mining. Chao Wang, V. Satuluri, and S. Parthasarathy. 2007. Local probabilistic models for link prediction. In Proceedings of the 7th IEEE International Conference on Data Mining."},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2020.09.002"},{"key":"e_1_2_1_50_1","volume-title":"Incremental group-level popularity prediction in online social networks. Work in Progress","author":"Wang Jingjing","year":"2021","unstructured":"Jingjing Wang , Wenjun Jiang , and Guojun Wang . 2021. Incremental group-level popularity prediction in online social networks. Work in Progress ( 2021 ). Jingjing Wang, Wenjun Jiang, and Guojun Wang. 2021. Incremental group-level popularity prediction in online social networks. Work in Progress (2021)."},{"key":"e_1_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11432-014-5237-y"},{"key":"e_1_2_1_52_1","volume-title":"Proceedings of the 12th ACM International Conference on Web Search and Data Mining. ACM, 537\u2013545","author":"Wu Xian","unstructured":"Xian Wu , Baoxu Shi , Yuxiao Dong , Chao Huang , and Nitesh V. Chawla . 2019. Neural tensor factorization for temporal interaction learning . In Proceedings of the 12th ACM International Conference on Web Search and Data Mining. ACM, 537\u2013545 . Xian Wu, Baoxu Shi, Yuxiao Dong, Chao Huang, and Nitesh V. Chawla. 2019. Neural tensor factorization for temporal interaction learning. In Proceedings of the 12th ACM International Conference on Web Search and Data Mining. ACM, 537\u2013545."},{"key":"e_1_2_1_53_1","volume-title":"Article 1","author":"Xia Peike","year":"2021","unstructured":"Peike Xia , Wenjun Jiang , Jie Wu , Surong Xiao , and Guojun Wang . 2020. Exploiting temporal dynamics in product reviews for dynamic sentiment prediction at the aspect level. ACM Transactions on Knowledge Discovery from Data 1, 1 , Article 1 ( 2021 ), 27 pages. https:\/\/doi.org\/10.1145\/3441451 10.1145\/3441451 Peike Xia, Wenjun Jiang, Jie Wu, Surong Xiao, and Guojun Wang. 2020. Exploiting temporal dynamics in product reviews for dynamic sentiment prediction at the aspect level. ACM Transactions on Knowledge Discovery from Data 1, 1, Article 1 (2021), 27 pages. https:\/\/doi.org\/10.1145\/3441451"},{"key":"e_1_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2014.2370942"},{"key":"e_1_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2015.7218618"},{"key":"e_1_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1145\/1935826.1935863"},{"key":"e_1_2_1_57_1","volume-title":"Proceedings of the 2015 54th IEEE Conference on Decision and Control. IEEE.","author":"Yeung Enoch","unstructured":"Enoch Yeung , Jongmin Kim , Jorge Gon\u00e7alves , and Richard M. Murray . 2015. Global network identification from reconstructed dynamical structure subnetworks: Applications to biochemical reaction networks . In Proceedings of the 2015 54th IEEE Conference on Decision and Control. IEEE. Enoch Yeung, Jongmin Kim, Jorge Gon\u00e7alves, and Richard M. Murray. 2015. Global network identification from reconstructed dynamical structure subnetworks: Applications to biochemical reaction networks. In Proceedings of the 2015 54th IEEE Conference on Decision and Control. IEEE."},{"key":"e_1_2_1_58_1","article-title":"Exploring weather data to predict activity attendance in event-based social network: From the organizer\u2019s view","volume":"1","author":"Zhang Jifeng","year":"2021","unstructured":"Jifeng Zhang , Wenjun Jiang , Jinrui Zhang , Jie Wu , and Guojun Wang . 2021 . Exploring weather data to predict activity attendance in event-based social network: From the organizer\u2019s view . ACM Transactions on the Web 1 , 1, Article 1 (2021), 25 pages. https:\/\/doi.org\/10.1145\/3440134 10.1145\/3440134 Jifeng Zhang, Wenjun Jiang, Jinrui Zhang, Jie Wu, and Guojun Wang. 2021. Exploring weather data to predict activity attendance in event-based social network: From the organizer\u2019s view. ACM Transactions on the Web 1, 1, Article 1 (2021), 25 pages. https:\/\/doi.org\/10.1145\/3440134","journal-title":"ACM Transactions on the Web"},{"key":"e_1_2_1_59_1","volume-title":"Proceedings of the 2018 IEEE International Conference on Data Mining. IEEE Computer Society.","author":"Zhou Shuo","unstructured":"Shuo Zhou , S. Erfani , and J. Bailey . 2018. Online CP Decomposition for Sparse Tensors . In Proceedings of the 2018 IEEE International Conference on Data Mining. IEEE Computer Society. Shuo Zhou, S. Erfani, and J. Bailey. 2018. Online CP Decomposition for Sparse Tensors. In Proceedings of the 2018 IEEE International Conference on Data Mining. IEEE Computer Society."},{"key":"e_1_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939763"},{"key":"e_1_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2016.2591009"}],"container-title":["ACM Transactions on Knowledge Discovery from Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3441645","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3441645","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T21:24:30Z","timestamp":1750195470000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3441645"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,21]]},"references-count":61,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2021,6,30]]}},"alternative-id":["10.1145\/3441645"],"URL":"https:\/\/doi.org\/10.1145\/3441645","relation":{},"ISSN":["1556-4681","1556-472X"],"issn-type":[{"value":"1556-4681","type":"print"},{"value":"1556-472X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,21]]},"assertion":[{"value":"2019-10-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-12-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-04-21","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}