{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:19:04Z","timestamp":1750220344969,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":22,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,9,9]],"date-time":"2021-09-09T00:00:00Z","timestamp":1631145600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"IBM","award":["Faculty award (Research)"],"award-info":[{"award-number":["Faculty award (Research)"]}]},{"name":"ONR Awards","award":["N00014-17-1-2995 and N00014-20-1-2738"],"award-info":[{"award-number":["N00014-17-1-2995 and N00014-20-1-2738"]}]},{"DOI":"10.13039\/100000001","name":"NSF (National Science Foundation)","doi-asserted-by":"publisher","award":["DMS-1737978, DGE-2039542, OAC-1828467, OAC-1931541, DGE17236021, SMA-1539302, OAC-1828467 and DGE-1906630"],"award-info":[{"award-number":["DMS-1737978, DGE-2039542, OAC-1828467, OAC-1931541, DGE17236021, SMA-1539302, OAC-1828467 and DGE-1906630"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000183","name":"Army Research Office","doi-asserted-by":"publisher","award":["No. W911NF2110032"],"award-info":[{"award-number":["No. W911NF2110032"]}],"id":[{"id":"10.13039\/100000183","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,9,9]]},"DOI":"10.1145\/3462203.3475929","type":"proceedings-article","created":{"date-parts":[[2021,8,19]],"date-time":"2021-08-19T00:54:15Z","timestamp":1629334455000},"page":"61-66","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Data-Driven Time Series Forecasting for Social Studies Using Spatio-Temporal Graph Neural Networks"],"prefix":"10.1145","author":[{"given":"Yi-Fan","family":"Li","sequence":"first","affiliation":[{"name":"Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas, Richardson, TX"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Dong","sequence":"additional","affiliation":[{"name":"Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas, Richardson, TX"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Latifur","family":"Khan","sequence":"additional","affiliation":[{"name":"Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas, Richardson, TX"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bhavani","family":"Thuraisingham","sequence":"additional","affiliation":[{"name":"Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas, Richardson, TX"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Patrick T.","family":"Brandt","sequence":"additional","affiliation":[{"name":"School of Economic, Political, and Policy Sciences University of Texas at Dallas Richardson, TX"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vito J.","family":"D'Orazio","sequence":"additional","affiliation":[{"name":"School of Economic, Political, and Policy Sciences University of Texas at Dallas Richardson, TX"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,9,9]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Modeling and forecasting of epidemic spreading: The case of Covid-19 and beyond. Chaos, solitons, and fractals 135","author":"Boccaletti Stefano","year":"2020","unstructured":"Stefano Boccaletti , William Ditto , Gabriel Mindlin , and Abdon Atangana . 2020. Modeling and forecasting of epidemic spreading: The case of Covid-19 and beyond. Chaos, solitons, and fractals 135 ( 2020 ), 109794. Stefano Boccaletti, William Ditto, Gabriel Mindlin, and Abdon Atangana. 2020. Modeling and forecasting of epidemic spreading: The case of Covid-19 and beyond. Chaos, solitons, and fractals 135 (2020), 109794."},{"volume-title":"Introduction to time series and forecasting","author":"Brockwell Peter J","key":"e_1_3_2_1_2_1","unstructured":"Peter J Brockwell , Richard A Davis , and Matthew V Calder . 2002. Introduction to time series and forecasting . Vol. 2 . Springer . Peter J Brockwell, Richard A Davis, and Matthew V Calder. 2002. Introduction to time series and forecasting. Vol. 2. Springer."},{"volume-title":"Deep learning in natural language processing","author":"Deng Li","key":"e_1_3_2_1_3_1","unstructured":"Li Deng and Yang Liu . 2018. Deep learning in natural language processing . Springer . Li Deng and Yang Liu. 2018. Deep learning in natural language processing. Springer."},{"key":"e_1_3_2_1_4_1","volume-title":"Deep learning on knowledge graph for recommender system: A survey. arXiv preprint arXiv:2004.00387","author":"Gao Yang","year":"2020","unstructured":"Yang Gao , Yi-Fan Li , Yu Lin , Hang Gao , and Latifur Khan . 2020. Deep learning on knowledge graph for recommender system: A survey. arXiv preprint arXiv:2004.00387 ( 2020 ). Yang Gao, Yi-Fan Li, Yu Lin, Hang Gao, and Latifur Khan. 2020. Deep learning on knowledge graph for recommender system: A survey. arXiv preprint arXiv:2004.00387 (2020)."},{"volume-title":"Deep learning","author":"Goodfellow Ian","key":"e_1_3_2_1_5_1","unstructured":"Ian Goodfellow , Yoshua Bengio , Aaron Courville , and Yoshua Bengio . 2016. Deep learning . Vol. 1 . MIT press Cambridge . Ian Goodfellow, Yoshua Bengio, Aaron Courville, and Yoshua Bengio. 2016. Deep learning. Vol. 1. MIT press Cambridge."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1198\/tast.2009.08199"},{"key":"e_1_3_2_1_7_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_8_1","doi-asserted-by":"publisher","DOI":"10.1177\/0022343319823860"},{"key":"e_1_3_2_1_9_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_10_1","volume-title":"Kipf and Max Welling","author":"Thomas","year":"2017","unstructured":"Thomas N. Kipf and Max Welling . 2017 . Semi-Supervised Classification with Graph Convolutional Networks. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings. OpenReview .net. https:\/\/openreview.net\/forum?id=SJU4ayYgl Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings. OpenReview.net. https:\/\/openreview.net\/forum?id=SJU4ayYgl"},{"key":"e_1_3_2_1_11_1","volume-title":"Proceedings of Preregister Workshop in 34th Conference on Neural Information Processing Systems.","author":"Li Yi-Fan","year":"2020","unstructured":"Yi-Fan Li , Yang Gao , Yu Lin , Zhuoyi Wang , and Latifur Khan . 2020 . Time Series Forecasting Using a Unified Spatial-Temporal Graph Convolutional Network . In Proceedings of Preregister Workshop in 34th Conference on Neural Information Processing Systems. Yi-Fan Li, Yang Gao, Yu Lin, Zhuoyi Wang, and Latifur Khan. 2020. Time Series Forecasting Using a Unified Spatial-Temporal Graph Convolutional Network. In Proceedings of Preregister Workshop in 34th Conference on Neural Information Processing Systems."},{"key":"e_1_3_2_1_12_1","volume-title":"Time Series Forecasting With Deep Learning: A Survey. arXiv preprint arXiv:2004.13408","author":"Lim Bryan","year":"2020","unstructured":"Bryan Lim and Stefan Zohren . 2020. Time Series Forecasting With Deep Learning: A Survey. arXiv preprint arXiv:2004.13408 ( 2020 ). Bryan Lim and Stefan Zohren. 2020. Time Series Forecasting With Deep Learning: A Survey. arXiv preprint arXiv:2004.13408 (2020)."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"crossref","unstructured":"Tom\u00e1\u0161 Mikolov Martin Karafi\u00e1t Luk\u00e1\u0161 Burget Jan \u010cernocky and Sanjeev Khudanpur. 2010. Recurrent neural network based language model. In Eleventh annual conference of the international speech communication association.  Tom\u00e1\u0161 Mikolov Martin Karafi\u00e1t Luk\u00e1\u0161 Burget Jan \u010cernocky and Sanjeev Khudanpur. 2010. Recurrent neural network based language model. In Eleventh annual conference of the international speech communication association.","DOI":"10.21437\/Interspeech.2010-343"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"crossref","unstructured":"Hogun Park and Jennifer Neville. 2019. Exploiting Interaction Links for Node Classification with Deep Graph Neural Networks.. In IJCAI. 3223--3230.  Hogun Park and Jennifer Neville. 2019. Exploiting Interaction Links for Node Classification with Deep Graph Neural Networks.. In IJCAI. 3223--3230.","DOI":"10.24963\/ijcai.2019\/447"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380257"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/78.650093"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLA.2018.00227"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1177\/0022343311431287"},{"key":"e_1_3_2_1_19_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 Lio , and Yoshua Bengio . 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 ( 2017 ). Petar Veli\u010dkovi\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_20_1","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)0733-947X(2003)129:6(664)"},{"key":"e_1_3_2_1_21_1","volume-title":"Spatial temporal graph convolutional networks for skeleton-based action recognition. arXiv preprint arXiv.1801.07455","author":"Yan Sijie","year":"2018","unstructured":"Sijie Yan , Yuanjun Xiong , and Dahua Lin . 2018. Spatial temporal graph convolutional networks for skeleton-based action recognition. arXiv preprint arXiv.1801.07455 ( 2018 ). Sijie Yan, Yuanjun Xiong, and Dahua Lin. 2018. Spatial temporal graph convolutional networks for skeleton-based action recognition. arXiv preprint arXiv.1801.07455 (2018)."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/505"}],"event":{"name":"GoodIT '21: Conference on Information Technology for Social Good","sponsor":["SIGCAS ACM Special Interest Group on Computers and Society"],"location":"Roma Italy","acronym":"GoodIT '21"},"container-title":["Proceedings of the Conference on Information Technology for Social Good"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3462203.3475929","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3462203.3475929","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3462203.3475929","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:17:01Z","timestamp":1750191421000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3462203.3475929"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,9]]},"references-count":22,"alternative-id":["10.1145\/3462203.3475929","10.1145\/3462203"],"URL":"https:\/\/doi.org\/10.1145\/3462203.3475929","relation":{},"subject":[],"published":{"date-parts":[[2021,9,9]]},"assertion":[{"value":"2021-09-09","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}