{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T00:47:15Z","timestamp":1775263635053,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":39,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,10,26]],"date-time":"2021-10-26T00:00:00Z","timestamp":1635206400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000183","name":"Army Research Office","doi-asserted-by":"publisher","award":["W911NF-21-1-0198"],"award-info":[{"award-number":["W911NF-21-1-0198"]}],"id":[{"id":"10.13039\/100000183","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"NSF (National Science Foundation)","doi-asserted-by":"publisher","award":["IIS1714741, CNS1815636, IIS1845081, IIS1907704, DRL2025244, IIS1928278, IIS1955285, IOS2107215, IOS2035472"],"award-info":[{"award-number":["IIS1714741, CNS1815636, IIS1845081, IIS1907704, DRL2025244, IIS1928278, IIS1955285, IOS2107215, IOS2035472"]}],"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":[[2021,10,26]]},"DOI":"10.1145\/3459637.3482225","type":"proceedings-article","created":{"date-parts":[[2021,11,15]],"date-time":"2021-11-15T15:31:14Z","timestamp":1636990274000},"page":"1202-1211","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":93,"title":["A Unified View on Graph Neural Networks as Graph Signal Denoising"],"prefix":"10.1145","author":[{"given":"Yao","family":"Ma","sequence":"first","affiliation":[{"name":"New Jersey Institute of Technology, Newark, NJ, USA"}]},{"given":"Xiaorui","family":"Liu","sequence":"additional","affiliation":[{"name":"Michigan State University, East Lansing, MI, USA"}]},{"given":"Tong","family":"Zhao","sequence":"additional","affiliation":[{"name":"University of Notre Dame, Notre Dame, OH, USA"}]},{"given":"Yozen","family":"Liu","sequence":"additional","affiliation":[{"name":"Snap Inc., Santa Monica, CA, USA"}]},{"given":"Jiliang","family":"Tang","sequence":"additional","affiliation":[{"name":"Michigan State University, East Lansing, MI, USA"}]},{"given":"Neil","family":"Shah","sequence":"additional","affiliation":[{"name":"Snap Inc., Seattle, WA, USA"}]}],"member":"320","published-online":{"date-parts":[[2021,10,30]]},"reference":[{"key":"e_1_3_2_1_1_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_2_1","volume-title":"Graph Unrolling Networks: Interpretable Neural Networks for Graph Signal Denoising. arXiv preprint arXiv:2006.01301","author":"Chen Siheng","year":"2020","unstructured":"Siheng Chen , Yonina C Eldar , and Lingxiao Zhao . 2020. Graph Unrolling Networks: Interpretable Neural Networks for Graph Signal Denoising. arXiv preprint arXiv:2006.01301 ( 2020 ). Siheng Chen, Yonina C Eldar, and Lingxiao Zhao. 2020. Graph Unrolling Networks: Interpretable Neural Networks for Graph Signal Denoising. arXiv preprint arXiv:2006.01301 (2020)."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/GlobalSIP.2014.7032244"},{"key":"e_1_3_2_1_4_1","volume-title":"Adaptive universal generalized pagerank graph neural network. arXiv preprint arXiv:2006.07988","author":"Chien Eli","year":"2020","unstructured":"Eli Chien , Jianhao Peng , Pan Li , and Olgica Milenkovic . 2020. Adaptive universal generalized pagerank graph neural network. arXiv preprint arXiv:2006.07988 ( 2020 ). Eli Chien, Jianhao Peng, Pan Li, and Olgica Milenkovic. 2020. Adaptive universal generalized pagerank graph neural network. arXiv preprint arXiv:2006.07988 (2020)."},{"key":"e_1_3_2_1_5_1","volume-title":"Principal neighbourhood aggregation for graph nets. arXiv preprint arXiv:2004.05718","author":"Corso Gabriele","year":"2020","unstructured":"Gabriele Corso , Luca Cavalleri , Dominique Beaini , Pietro Li\u00f2 , and Petar Velivc kovi\u0107. 2020. Principal neighbourhood aggregation for graph nets. arXiv preprint arXiv:2004.05718 ( 2020 ). Gabriele Corso, Luca Cavalleri, Dominique Beaini, Pietro Li\u00f2, and Petar Velivc kovi\u0107. 2020. Principal neighbourhood aggregation for graph nets. arXiv preprint arXiv:2004.05718 (2020)."},{"key":"e_1_3_2_1_6_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_7_1","volume-title":"Barakeel Fanseu Kamhoua, and James Cheng","author":"Fu Guoji","year":"2020","unstructured":"Guoji Fu , Yifan Hou , Jian Zhang , Kaili Ma , Barakeel Fanseu Kamhoua, and James Cheng . 2020 . Understanding graph neural networks from graph signal denoising perspectives. arXiv preprint arXiv:2006.04386 (2020). Guoji Fu, Yifan Hou, Jian Zhang, Kaili Ma, Barakeel Fanseu Kamhoua, and James Cheng. 2020. Understanding graph neural networks from graph signal denoising perspectives. arXiv preprint arXiv:2006.04386 (2020)."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219947"},{"key":"e_1_3_2_1_9_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_10_1","unstructured":"Will Hamilton Zhitao Ying and Jure Leskovec. 2017. Inductive representation learning on large graphs. In NeurIPS. 1024--1034.  Will Hamilton Zhitao Ying and Jure Leskovec. 2017. Inductive representation learning on large graphs. In NeurIPS. 1024--1034."},{"key":"e_1_3_2_1_11_1","volume-title":"Deep convolutional networks on graph-structured data. arXiv preprint arXiv:1506.05163","author":"Henaff Mikael","year":"2015","unstructured":"Mikael Henaff , Joan Bruna , and Yann LeCun . 2015. Deep convolutional networks on graph-structured data. arXiv preprint arXiv:1506.05163 ( 2015 ). Mikael Henaff, Joan Bruna, and Yann LeCun. 2015. Deep convolutional networks on graph-structured data. arXiv preprint arXiv:1506.05163 (2015)."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3018661.3018667"},{"key":"e_1_3_2_1_13_1","volume-title":"Graph Structure Learning for Robust Graph Neural Networks. arXiv preprint arXiv:2005.10203","author":"Jin Wei","year":"2020","unstructured":"Wei Jin , Yao Ma , Xiaorui Liu , Xianfeng Tang , Suhang Wang , and Jiliang Tang . 2020. Graph Structure Learning for Robust Graph Neural Networks. arXiv preprint arXiv:2005.10203 ( 2020 ). Wei Jin, Yao Ma, Xiaorui Liu, Xianfeng Tang, Suhang Wang, and Jiliang Tang. 2020. Graph Structure Learning for Robust Graph Neural Networks. arXiv preprint arXiv:2005.10203 (2020)."},{"key":"e_1_3_2_1_14_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_15_1","volume-title":"Predict then propagate: Graph neural networks meet personalized pagerank. arXiv preprint arXiv:1810.05997","author":"Klicpera Johannes","year":"2018","unstructured":"Johannes Klicpera , Aleksandar Bojchevski , and Stephan G\u00fcnnemann . 2018. Predict then propagate: Graph neural networks meet personalized pagerank. arXiv preprint arXiv:1810.05997 ( 2018 ). Johannes Klicpera, Aleksandar Bojchevski, and Stephan G\u00fcnnemann. 2018. Predict then propagate: Graph neural networks meet personalized pagerank. arXiv preprint arXiv:1810.05997 (2018)."},{"key":"e_1_3_2_1_16_1","volume-title":"Is Homophily a Necessity for Graph Neural Networks? arXiv preprint arXiv:2106.06134","author":"Ma Yao","year":"2021","unstructured":"Yao Ma , Xiaorui Liu , Neil Shah , and Jiliang Tang . 2021. Is Homophily a Necessity for Graph Neural Networks? arXiv preprint arXiv:2106.06134 ( 2021 ). Yao Ma, Xiaorui Liu, Neil Shah, and Jiliang Tang. 2021. Is Homophily a Necessity for Graph Neural Networks? arXiv preprint arXiv:2106.06134 (2021)."},{"key":"e_1_3_2_1_17_1","volume-title":"Deep learning on graphs","author":"Ma Yao","unstructured":"Yao Ma and Jiliang Tang . 2021. Deep learning on graphs . Cambridge University Press . Yao Ma and Jiliang Tang. 2021. Deep learning on graphs. Cambridge University Press."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.576"},{"key":"e_1_3_2_1_19_1","volume-title":"Revisiting graph neural networks: All we have is low-pass filters. arXiv preprint arXiv:1905.09550","author":"Takanori Maehara Hoang","year":"2019","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_20_1","volume-title":"Yu Lei, and Bo Yang.","author":"Pei Hongbin","year":"2020","unstructured":"Hongbin Pei , Bingzhe Wei , Kevin Chen-Chuan Chang , Yu Lei, and Bo Yang. 2020 . Geom-gcn : Geometric graph convolutional networks. arXiv preprint arXiv:2002.05287 (2020). Hongbin Pei, Bingzhe Wei, Kevin Chen-Chuan Chang, Yu Lei, and Bo Yang. 2020. Geom-gcn: Geometric graph convolutional networks. arXiv preprint arXiv:2002.05287 (2020)."},{"key":"e_1_3_2_1_21_1","volume-title":"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_22_1","volume-title":"Collective classification in network data. AI magazine","author":"Sen Prithviraj","year":"2008","unstructured":"Prithviraj Sen , Galileo Namata , Mustafa Bilgic , Lise Getoor , Brian Galligher , and Tina Eliassi-Rad . 2008. Collective classification in network data. AI magazine , Vol. 29 , 3 ( 2008 ), 93--93. Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad. 2008. Collective classification in network data. AI magazine, Vol. 29, 3 (2008), 93--93."},{"key":"e_1_3_2_1_23_1","volume-title":"Attributed and Class-Assortative Graph Generation to Facilitate Introspection of Graph Neural Networks. KDD Mining and Learning with Graphs","author":"Shah Neil","year":"2020","unstructured":"Neil Shah . 2020. Scale-Free , Attributed and Class-Assortative Graph Generation to Facilitate Introspection of Graph Neural Networks. KDD Mining and Learning with Graphs ( 2020 ). Neil Shah. 2020. Scale-Free, Attributed and Class-Assortative Graph Generation to Facilitate Introspection of Graph Neural Networks. KDD Mining and Learning with Graphs (2020)."},{"key":"e_1_3_2_1_24_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\u00fcnnemann . 2018. Pitfalls of graph neural network evaluation. arXiv preprint arXiv:1811.05868 ( 2018 ). Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, and Stephan G\u00fcnnemann. 2018. Pitfalls of graph neural network evaluation. arXiv preprint arXiv:1811.05868 (2018)."},{"key":"e_1_3_2_1_25_1","volume-title":"The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains","author":"Shuman David I","year":"2013","unstructured":"David I Shuman , Sunil K Narang , Pascal Frossard , Antonio Ortega , and Pierre Vandergheynst . 2013. The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains . IEEE signal processing magazine, Vol. 30 , 3 ( 2013 ), 83--98. David I Shuman, Sunil K Narang, Pascal Frossard, Antonio Ortega, and Pierre Vandergheynst. 2013. The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine, Vol. 30, 3 (2013), 83--98."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1214\/13-AOS1189"},{"key":"e_1_3_2_1_27_1","volume-title":"Graph attention networks. arXiv preprint arXiv:1710.10903","author":"Petar Velivc","year":"2017","unstructured":"Petar Velivc kovi\u0107, Guillem Cucurull , Arantxa Casanova , Adriana Romero , Pietro Lio , and Yoshua Bengio . 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 ( 2017 ). Petar Velivc kovi\u0107, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.5555\/2946645.3007058"},{"key":"e_1_3_2_1_29_1","volume-title":"Adversarial examples on graph data: Deep insights into attack and defense. arXiv preprint arXiv:1903.01610","author":"Wu Huijun","year":"2019","unstructured":"Huijun Wu , Chen Wang , Yuriy Tyshetskiy , Andrew Docherty , Kai Lu , and Liming Zhu . 2019b. Adversarial examples on graph data: Deep insights into attack and defense. arXiv preprint arXiv:1903.01610 ( 2019 ). Huijun Wu, Chen Wang, Yuriy Tyshetskiy, Andrew Docherty, Kai Lu, and Liming Zhu. 2019b. Adversarial examples on graph data: Deep insights into attack and defense. arXiv preprint arXiv:1903.01610 (2019)."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330950"},{"key":"e_1_3_2_1_31_1","volume-title":"International conference on machine learning. PMLR, 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. PMLR, 40--48 . Zhilin Yang, William Cohen, and Ruslan Salakhudinov. 2016. Revisiting semi-supervised learning with graph embeddings. In International conference on machine learning. PMLR, 40--48."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219890"},{"key":"e_1_3_2_1_33_1","unstructured":"Zhitao Ying Jiaxuan You Christopher Morris Xiang Ren Will Hamilton and Jure Leskovec. 2018b. 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. 2018b. Hierarchical graph representation learning with differentiable pooling. In Advances in neural information processing systems. 4800--4810."},{"key":"e_1_3_2_1_34_1","volume-title":"Pairnorm: Tackling oversmoothing in gnns. arXiv preprint arXiv:1909.12223","author":"Zhao Lingxiao","year":"2019","unstructured":"Lingxiao Zhao and Leman Akoglu . 2019 . Pairnorm: Tackling oversmoothing in gnns. arXiv preprint arXiv:1909.12223 (2019). Lingxiao Zhao and Leman Akoglu. 2019. Pairnorm: Tackling oversmoothing in gnns. arXiv preprint arXiv:1909.12223 (2019)."},{"key":"e_1_3_2_1_35_1","volume-title":"Data Augmentation for Graph Neural Networks. arXiv preprint arXiv:2006.06830","author":"Zhao Tong","year":"2020","unstructured":"Tong Zhao , Yozen Liu , Leonardo Neves , Oliver Woodford , Meng Jiang , and Neil Shah . 2020. Data Augmentation for Graph Neural Networks. arXiv preprint arXiv:2006.06830 ( 2020 ). Tong Zhao, Yozen Liu, Leonardo Neves, Oliver Woodford, Meng Jiang, and Neil Shah. 2020. Data Augmentation for Graph Neural Networks. arXiv preprint arXiv:2006.06830 (2020)."},{"key":"e_1_3_2_1_36_1","volume-title":"Graph neural networks with heterophily. arXiv preprint arXiv:2009.13566","author":"Zhu Jiong","year":"2020","unstructured":"Jiong Zhu , Ryan A Rossi , Anup Rao , Tung Mai , Nedim Lipka , Nesreen K Ahmed , and Danai Koutra . 2020a. Graph neural networks with heterophily. arXiv preprint arXiv:2009.13566 ( 2020 ). Jiong Zhu, Ryan A Rossi, Anup Rao, Tung Mai, Nedim Lipka, Nesreen K Ahmed, and Danai Koutra. 2020a. Graph neural networks with heterophily. arXiv preprint arXiv:2009.13566 (2020)."},{"key":"e_1_3_2_1_37_1","volume-title":"Beyond homophily in graph neural networks: Current limitations and effective designs. arXiv preprint arXiv:2006.11468","author":"Zhu Jiong","year":"2020","unstructured":"Jiong Zhu , Yujun Yan , Lingxiao Zhao , Mark Heimann , Leman Akoglu , and Danai Koutra . 2020b. Beyond homophily in graph neural networks: Current limitations and effective designs. arXiv preprint arXiv:2006.11468 ( 2020 ). Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, and Danai Koutra. 2020b. Beyond homophily in graph neural networks: Current limitations and effective designs. arXiv preprint arXiv:2006.11468 (2020)."},{"key":"e_1_3_2_1_38_1","volume-title":"Interpreting and Unifying Graph Neural Networks with An Optimization Framework. arXiv preprint arXiv:2101.11859","author":"Zhu Meiqi","year":"2021","unstructured":"Meiqi Zhu , Xiao Wang , Chuan Shi , Houye Ji , and Peng Cui . 2021. Interpreting and Unifying Graph Neural Networks with An Optimization Framework. arXiv preprint arXiv:2101.11859 ( 2021 ). Meiqi Zhu, Xiao Wang, Chuan Shi, Houye Ji, and Peng Cui. 2021. Interpreting and Unifying Graph Neural Networks with An Optimization Framework. arXiv preprint arXiv:2101.11859 (2021)."},{"key":"e_1_3_2_1_39_1","volume-title":"Adversarial attacks on graph neural networks via meta learning. arXiv preprint arXiv:1902.08412","author":"Z\u00fcgner Daniel","year":"2019","unstructured":"Daniel Z\u00fcgner and Stephan G\u00fcnnemann . 2019. Adversarial attacks on graph neural networks via meta learning. arXiv preprint arXiv:1902.08412 ( 2019 ). Daniel Z\u00fcgner and Stephan G\u00fcnnemann. 2019. Adversarial attacks on graph neural networks via meta learning. arXiv preprint arXiv:1902.08412 (2019)."}],"event":{"name":"CIKM '21: The 30th ACM International Conference on Information and Knowledge Management","location":"Virtual Event Queensland Australia","acronym":"CIKM '21","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 30th ACM International Conference on Information & Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3459637.3482225","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3459637.3482225","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3459637.3482225","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:12:12Z","timestamp":1750191132000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3459637.3482225"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,26]]},"references-count":39,"alternative-id":["10.1145\/3459637.3482225","10.1145\/3459637"],"URL":"https:\/\/doi.org\/10.1145\/3459637.3482225","relation":{},"subject":[],"published":{"date-parts":[[2021,10,26]]},"assertion":[{"value":"2021-10-30","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}