{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T13:20:25Z","timestamp":1775913625087,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":37,"publisher":"ACM","license":[{"start":{"date-parts":[[2019,8,27]],"date-time":"2019-08-27T00:00:00Z","timestamp":1566864000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Zhejing University ZJU Research","award":["083650"],"award-info":[{"award-number":["083650"]}]},{"name":"UIUC OVCR CCIL","award":["434S34"],"award-info":[{"award-number":["434S34"]}]},{"name":"UIUC Advanced Digital Sciences Center"},{"name":"Futurewei Technologies","award":["HF2017060011, 094013"],"award-info":[{"award-number":["HF2017060011, 094013"]}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["IIS 16-19302, IIS 16-33755"],"award-info":[{"award-number":["IIS 16-19302, IIS 16-33755"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"name":"UIUC CSBS","award":["434C8U"],"award-info":[{"award-number":["434C8U"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2019,8,27]]},"DOI":"10.1145\/3341161.3342859","type":"proceedings-article","created":{"date-parts":[[2020,1,15]],"date-time":"2020-01-15T21:07:04Z","timestamp":1579122424000},"page":"137-144","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":56,"title":["Meta-GNN"],"prefix":"10.1145","author":[{"given":"Aravind","family":"Sankar","sequence":"first","affiliation":[{"name":"University of Illinois"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinyang","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Illinois"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kevin Chen-Chuan","family":"Chang","sequence":"additional","affiliation":[{"name":"University of Illinois"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2020,1,15]]},"reference":[{"key":"e_1_3_2_1_1_1","first-page":"321","volume-title":"Learning with local and global consistency,\" in Advances in neural information processing systems","author":"Zhou D.","year":"2004","unstructured":"D. Zhou , O. Bousquet , T. N. Lal , J. Weston , and B. Sch\u00f6lkopf , \" Learning with local and global consistency,\" in Advances in neural information processing systems , 2004 , pp. 321 -- 328 . D. Zhou, O. Bousquet, T. N. Lal, J. Weston, and B. Sch\u00f6lkopf, \"Learning with local and global consistency,\" in Advances in neural information processing systems, 2004, pp. 321--328."},{"key":"e_1_3_2_1_2_1","first-page":"817","volume-title":"ACM","author":"Tang L.","year":"2009","unstructured":"L. Tang and H. Liu , \" Relational learning via latent social dimensions,\" in Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining . ACM , 2009 , pp. 817 -- 826 . L. Tang and H. Liu, \"Relational learning via latent social dimensions,\" in Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2009, pp. 817--826."},{"key":"e_1_3_2_1_3_1","volume-title":"Improving latent user models in online social media,\" arXiv preprint arXiv.1711.11124","author":"Krishnan A.","year":"2017","unstructured":"A. Krishnan , A. Sharma , and H. Sundaram , \" Improving latent user models in online social media,\" arXiv preprint arXiv.1711.11124 , 2017 . A. Krishnan, A. Sharma, and H. Sundaram, \"Improving latent user models in online social media,\" arXiv preprint arXiv.1711.11124, 2017."},{"key":"e_1_3_2_1_4_1","first-page":"570","volume-title":"Graph regularized transductive classification on heterogeneous information networks,\" in Joint European Conference on Machine Learning and Knowledge Discovery in Databases","author":"Ji M.","year":"2010","unstructured":"M. Ji , Y. Sun , M. Danilevsky , J. Han , and J. Gao , \" Graph regularized transductive classification on heterogeneous information networks,\" in Joint European Conference on Machine Learning and Knowledge Discovery in Databases . Springer , 2010 , pp. 570 -- 586 . M. Ji, Y. Sun, M. Danilevsky, J. Han, and J. Gao, \"Graph regularized transductive classification on heterogeneous information networks,\" in Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 2010, pp. 570--586."},{"key":"e_1_3_2_1_5_1","first-page":"1245","article-title":"Bridging collaborative filtering and semi-supervised learning: a neural approach for poi recommendation,\" in Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Yang C.","year":"2017","unstructured":"C. Yang , L. Bai , C. Zhang , Q. Yuan , and J. Han , \" Bridging collaborative filtering and semi-supervised learning: a neural approach for poi recommendation,\" in Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . ACM , 2017 , pp. 1245 -- 1254 . C. Yang, L. Bai, C. Zhang, Q. Yuan, and J. Han, \"Bridging collaborative filtering and semi-supervised learning: a neural approach for poi recommendation,\" in Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2017, pp. 1245--1254.","journal-title":"ACM"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3269264"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.14778\/3402707.3402736"},{"key":"e_1_3_2_1_8_1","first-page":"1993","article-title":"Diffusion-convolutional neural networks","author":"Atwood J.","year":"2016","unstructured":"J. Atwood and D. Towsley , \" Diffusion-convolutional neural networks ,\" in Advances in Neural Information Processing Systems , 2016 , pp. 1993 -- 2001 . J. Atwood and D. Towsley, \"Diffusion-convolutional neural networks,\" in Advances in Neural Information Processing Systems, 2016, pp. 1993--2001.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_9_1","volume-title":"Semi-supervised classification with graph convolutional networks,\" in International Conference for Learning Representations (ICLR)","author":"Kipf T. N.","year":"2017","unstructured":"T. N. Kipf and M. Welling , \" Semi-supervised classification with graph convolutional networks,\" in International Conference for Learning Representations (ICLR) , 2017 . T. N. Kipf and M. Welling, \"Semi-supervised classification with graph convolutional networks,\" in International Conference for Learning Representations (ICLR), 2017."},{"key":"e_1_3_2_1_10_1","volume-title":"Graph attention networks,\" arXiv preprint arXiv:1710.10903","author":"Veli\u010dkovi\u0107 P.","year":"2017","unstructured":"P. Veli\u010dkovi\u0107 , G. Cucurull , A. Casanova , A. Romero , P. Li\u00f2 , and Y. Bengio , \" Graph attention networks,\" arXiv preprint arXiv:1710.10903 , 2017 . P. Veli\u010dkovi\u0107, G. Cucurull, A. Casanova, A. Romero, P. Li\u00f2, and Y. Bengio, \"Graph attention networks,\" arXiv preprint arXiv:1710.10903, 2017."},{"key":"e_1_3_2_1_11_1","first-page":"1621","volume-title":"International World Wide Web Conferences Steering Committee","author":"Li X.","year":"2017","unstructured":"X. Li , Y. Wu , M. Ester , B. Kao , X. Wang , and Y. Zheng , \" Semi-supervised clustering in attributed heterogeneous information networks,\" in Proceedings of the 26th International Conference on World Wide Web . International World Wide Web Conferences Steering Committee , 2017 , pp. 1621 -- 1629 . X. Li, Y. Wu, M. Ester, B. Kao, X. Wang, and Y. Zheng, \"Semi-supervised clustering in attributed heterogeneous information networks,\" in Proceedings of the 26th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 2017, pp. 1621--1629."},{"key":"e_1_3_2_1_12_1","first-page":"746","volume-title":"IEEE","author":"Hu J.","year":"2019","unstructured":"J. Hu , R. Cheng , K. C.-C. Chang , A. Sankar , Y. Fang , and B. Y. Lam , \" Discovering maximal motif cliques in large heterogeneous information networks,\" in 2019 IEEE 35th International Conference on Data Engineering (ICDE) . IEEE , 2019 , pp. 746 -- 757 . J. Hu, R. Cheng, K. C.-C. Chang, A. Sankar, Y. Fang, and B. Y. Lam, \"Discovering maximal motif cliques in large heterogeneous information networks,\" in 2019 IEEE 35th International Conference on Data Engineering (ICDE). IEEE, 2019, pp. 746--757."},{"key":"e_1_3_2_1_13_1","volume-title":"Neural machine translation by jointly learning to align and translate,\" arXiv preprint arXiv.1409.0473","author":"Bahdanau D.","year":"2014","unstructured":"D. Bahdanau , K. Cho , and Y. Bengio , \" Neural machine translation by jointly learning to align and translate,\" arXiv preprint arXiv.1409.0473 , 2014 . D. Bahdanau, K. Cho, and Y. Bengio, \"Neural machine translation by jointly learning to align and translate,\" arXiv preprint arXiv.1409.0473, 2014."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.5555\/1248547.1248632"},{"key":"e_1_3_2_1_15_1","first-page":"1239","volume-title":"Semi-supervised learning,\" in Academic Press Library in Signal Processing","author":"Zhou X.","year":"2014","unstructured":"X. Zhou and M. Belkin , \" Semi-supervised learning,\" in Academic Press Library in Signal Processing . Elsevier , 2014 , vol. 1 , pp. 1239 -- 1269 . X. Zhou and M. Belkin, \"Semi-supervised learning,\" in Academic Press Library in Signal Processing. Elsevier, 2014, vol. 1, pp. 1239--1269."},{"key":"e_1_3_2_1_16_1","first-page":"701","volume-title":"ACM","author":"Perozzi B.","year":"2014","unstructured":"B. Perozzi , R. Al-Rfou , and S. Skiena , \" Deepwalk: Online learning of social representations,\" in Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining . ACM , 2014 , pp. 701 -- 710 . B. Perozzi, R. Al-Rfou, and S. Skiena, \"Deepwalk: Online learning of social representations,\" in Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2014, pp. 701--710."},{"key":"e_1_3_2_1_17_1","first-page":"855","volume-title":"ACM","author":"Grover A.","year":"2016","unstructured":"A. Grover and J. Leskovec , \" node2vec: Scalable feature learning for networks,\" in Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining . ACM , 2016 , pp. 855 -- 864 . A. Grover and J. Leskovec, \"node2vec: Scalable feature learning for networks,\" in Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2016, pp. 855--864."},{"key":"e_1_3_2_1_18_1","volume-title":"IEEE","author":"Sankar A.","year":"2019","unstructured":"A. Sankar , A. Krishnan , Z. He , and C. Yang , \" Rase: Relationship aware social embedding,\" in 2019 International Joint Conference on Neural Networks (IJCNN) . IEEE , 2019 . A. Sankar, A. Krishnan, Z. He, and C. Yang, \"Rase: Relationship aware social embedding,\" in 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 2019."},{"key":"e_1_3_2_1_19_1","first-page":"05163","article-title":"Deep convolutional networks on graph-structured data","volume":"1506","author":"Henaff M.","year":"2015","unstructured":"M. Henaff , J. Bruna , and Y. LeCun , \" Deep convolutional networks on graph-structured data ,\" CoRR , vol. abs\/ 1506 . 05163 , 2015 . M. Henaff, J. Bruna, and Y. LeCun, \"Deep convolutional networks on graph-structured data,\" CoRR, vol. abs\/1506.05163, 2015.","journal-title":"CoRR"},{"key":"e_1_3_2_1_20_1","volume-title":"Robust spatial filtering with graph convolutional neural networks,\" arXiv preprint arXiv.1703.00792","author":"Such F. P.","year":"2017","unstructured":"F. P. Such , S. Sah , M. Dominguez , S. Pillai , C. Zhang , A. Michael , N. Cahill , and R. Ptucha , \" Robust spatial filtering with graph convolutional neural networks,\" arXiv preprint arXiv.1703.00792 , 2017 . F. P. Such, S. Sah, M. Dominguez, S. Pillai, C. Zhang, A. Michael, N. Cahill, and R. Ptucha, \"Robust spatial filtering with graph convolutional neural networks,\" arXiv preprint arXiv.1703.00792, 2017."},{"key":"e_1_3_2_1_21_1","first-page":"1025","article-title":"Inductive representation learning on large graphs","volume":"30","author":"Hamilton W.","year":"2017","unstructured":"W. Hamilton , Z. Ying , and J. Leskovec , \" Inductive representation learning on large graphs ,\" in Advances in Neural Information Processing Systems 30 , 2017 , pp. 1025 -- 1035 . W. Hamilton, Z. Ying, and J. Leskovec, \"Inductive representation learning on large graphs,\" in Advances in Neural Information Processing Systems 30, 2017, pp. 1025--1035.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_22_1","volume-title":"Dynamic graph representation learning via self-attention networks,\" arXiv preprint arXiv:1812.09430","author":"Sankar A.","year":"2018","unstructured":"A. Sankar , Y. Wu , L. Gou , W. Zhang , and H. Yang , \" Dynamic graph representation learning via self-attention networks,\" arXiv preprint arXiv:1812.09430 , 2018 . A. Sankar, Y. Wu, L. Gou, W. Zhang, and H. Yang, \"Dynamic graph representation learning via self-attention networks,\" arXiv preprint arXiv:1812.09430, 2018."},{"key":"e_1_3_2_1_23_1","first-page":"135","volume-title":"ACM","author":"Dong Y.","year":"2017","unstructured":"Y. Dong , N. V. Chawla , and A. Swami , \" metapath2vec: Scalable representation learning for heterogeneous networks,\" in Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . ACM , 2017 , pp. 135 -- 144 . Y. Dong, N. V. Chawla, and A. Swami, \"metapath2vec: Scalable representation learning for heterogeneous networks,\" in Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2017, pp. 135--144."},{"key":"e_1_3_2_1_24_1","volume-title":"Semi-supervised learning over heterogeneous information networks by ensemble of meta-graph guided random walks.\" IJCAI","author":"Jiang H.","year":"2017","unstructured":"H. Jiang , Y. Song , C. Wang , M. Zhang , and Y. Sun , \" Semi-supervised learning over heterogeneous information networks by ensemble of meta-graph guided random walks.\" IJCAI , 2017 . H. Jiang, Y. Song, C. Wang, M. Zhang, and Y. Sun, \"Semi-supervised learning over heterogeneous information networks by ensemble of meta-graph guided random walks.\" IJCAI, 2017."},{"key":"e_1_3_2_1_25_1","first-page":"1567","volume-title":"ACM","author":"Kong X.","year":"2012","unstructured":"X. Kong , P. S. Yu , Y. Ding , and D. J. Wild , \" Meta path-based collective classification in heterogeneous information networks,\" in Proceedings of the 21st ACM international conference on Information and knowledge management . ACM , 2012 , pp. 1567 -- 1571 . X. Kong, P. S. Yu, Y. Ding, and D. J. Wild, \"Meta path-based collective classification in heterogeneous information networks,\" in Proceedings of the 21st ACM international conference on Information and knowledge management. ACM, 2012, pp. 1567--1571."},{"key":"e_1_3_2_1_26_1","first-page":"2485","article-title":"Column networks for collective classification","author":"Pham T.","year":"2017","unstructured":"T. Pham , T. Tran , D. Q. Phung , and S. Venkatesh , \" Column networks for collective classification .\" in AAAI , 2017 , pp. 2485 -- 2491 . T. Pham, T. Tran, D. Q. Phung, and S. Venkatesh, \"Column networks for collective classification.\" in AAAI, 2017, pp. 2485--2491.","journal-title":"AAAI"},{"key":"e_1_3_2_1_27_1","first-page":"277","volume-title":"2016 IEEE 32nd International Conference on. IEEE","author":"Fang Y.","year":"2016","unstructured":"Y. Fang , W. Lin , V. W. Zheng , M. Wu , K. C.-C. Chang , and X.-L. Li , \"Semantic proximity search on graphs with metagraph-based learning,\" in Data Engineering (ICDE) , 2016 IEEE 32nd International Conference on. IEEE , 2016 , pp. 277 -- 288 . Y. Fang, W. Lin, V. W. Zheng, M. Wu, K. C.-C. Chang, and X.-L. Li, \"Semantic proximity search on graphs with metagraph-based learning,\" in Data Engineering (ICDE), 2016 IEEE 32nd International Conference on. IEEE, 2016, pp. 277--288."},{"key":"e_1_3_2_1_28_1","first-page":"918","volume-title":"SIAM","author":"Wan M.","year":"2015","unstructured":"M. Wan , Y. Ouyang , L. Kaplan , and J. Han , \" Graph regularized meta-path based transductive regression in heterogeneous information network,\" in Proceedings of the 2015 SIAM International Conference on Data Mining . SIAM , 2015 , pp. 918 -- 926 . M. Wan, Y. Ouyang, L. Kaplan, and J. Han, \"Graph regularized meta-path based transductive regression in heterogeneous information network,\" in Proceedings of the 2015 SIAM International Conference on Data Mining. SIAM, 2015, pp. 918--926."},{"key":"e_1_3_2_1_29_1","first-page":"1797","volume-title":"ACM","author":"Fu T.-y.","year":"2017","unstructured":"T.-y. Fu , W.-C. Lee , and Z. Lei , \" Hin2vec: Explore meta-paths in heterogeneous information networks for representation learning,\" in Proceedings of the 2017 ACM on Conference on Information and Knowledge Management . ACM , 2017 , pp. 1797 -- 1806 . T.-y. Fu, W.-C. Lee, and Z. Lei, \"Hin2vec: Explore meta-paths in heterogeneous information networks for representation learning,\" in Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. ACM, 2017, pp. 1797--1806."},{"key":"e_1_3_2_1_30_1","first-page":"144","volume-title":"SIAM","author":"Shi Y.","year":"2018","unstructured":"Y. Shi , H. Gui , Q. Zhu , L. Kaplan , and J. Han , \" Aspem: Embedding learning by aspects in heterogeneous information networks,\" in Proceedings of the 2018 SIAM International Conference on Data Mining . SIAM , 2018 , pp. 144 -- 152 . Y. Shi, H. Gui, Q. Zhu, L. Kaplan, and J. Han, \"Aspem: Embedding learning by aspects in heterogeneous information networks,\" in Proceedings of the 2018 SIAM International Conference on Data Mining. SIAM, 2018, pp. 144--152."},{"issue":"1","key":"e_1_3_2_1_31_1","first-page":"3","article-title":"Main-memory triangle computations for very large (sparse (power-law)) graphs","volume":"407","author":"Latapy M.","year":"2008","unstructured":"M. Latapy , \" Main-memory triangle computations for very large (sparse (power-law)) graphs ,\" Theoretical Computer Science , vol. 407 , no. 1 -- 3 , pp. 458--473, 2008 . M. Latapy, \"Main-memory triangle computations for very large (sparse (power-law)) graphs,\" Theoretical Computer Science, vol. 407, no. 1--3, pp. 458--473, 2008.","journal-title":"Theoretical Computer Science"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.5555\/2794367.2794368"},{"key":"e_1_3_2_1_33_1","volume-title":"Devin et al., \"Tensorflow: Large-scale machine learning on heterogeneous distributed systems,\" arXiv preprint arXiv.1603.04467","author":"Abadi M.","year":"2016","unstructured":"M. Abadi , A. Agarwal , P. Barham , E. Brevdo , Z. Chen , C. Citro , G. S. Corrado , A. Davis , J. Dean , M. Devin et al., \"Tensorflow: Large-scale machine learning on heterogeneous distributed systems,\" arXiv preprint arXiv.1603.04467 , 2016 . M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin et al., \"Tensorflow: Large-scale machine learning on heterogeneous distributed systems,\" arXiv preprint arXiv.1603.04467, 2016."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1009953814988"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/2827872"},{"key":"e_1_3_2_1_36_1","volume-title":"Adam: A method for stochastic optimization,\" in Proceedings of the 3rd International Conference on Learning Representations (ICLR)","author":"Kingma D. P.","year":"2014","unstructured":"D. P. Kingma and J. Ba , \" Adam: A method for stochastic optimization,\" in Proceedings of the 3rd International Conference on Learning Representations (ICLR) , 2014 . D. P. Kingma and J. Ba, \"Adam: A method for stochastic optimization,\" in Proceedings of the 3rd International Conference on Learning Representations (ICLR), 2014."},{"key":"e_1_3_2_1_37_1","unstructured":"A. Sankar X. Zhang and K. C.-C. Chang \"Motif-based convolutional neural network on graphs \" arXiv preprint arXiv.1711.05697 2017.  A. Sankar X. Zhang and K. C.-C. Chang \"Motif-based convolutional neural network on graphs \" arXiv preprint arXiv.1711.05697 2017."}],"event":{"name":"ASONAM '19: International Conference on Advances in Social Networks Analysis and Mining","location":"Vancouver British Columbia Canada","acronym":"ASONAM '19","sponsor":["SIGKDD ACM Special Interest Group on Knowledge Discovery in Data","IEEE CS"]},"container-title":["Proceedings of the 2019 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3341161.3342859","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3341161.3342859","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3341161.3342859","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:38:24Z","timestamp":1750199904000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3341161.3342859"}},"subtitle":["metagraph neural network for semi-supervised learning in attributed heterogeneous information networks"],"short-title":[],"issued":{"date-parts":[[2019,8,27]]},"references-count":37,"alternative-id":["10.1145\/3341161.3342859","10.1145\/3341161"],"URL":"https:\/\/doi.org\/10.1145\/3341161.3342859","relation":{},"subject":[],"published":{"date-parts":[[2019,8,27]]},"assertion":[{"value":"2020-01-15","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}