{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T18:40:43Z","timestamp":1772908843216,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":35,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,10,26]],"date-time":"2022-10-26T00:00:00Z","timestamp":1666742400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,11,2]]},"DOI":"10.1145\/3533271.3561751","type":"proceedings-article","created":{"date-parts":[[2022,10,20]],"date-time":"2022-10-20T22:20:22Z","timestamp":1666304422000},"page":"308-316","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Temporal Bipartite Graph Neural Networks for Bond Prediction"],"prefix":"10.1145","author":[{"given":"Dan","family":"Zhou","sequence":"first","affiliation":[{"name":"New Jersey Institute of Technology, US"}]},{"given":"Ajim","family":"Uddin","sequence":"additional","affiliation":[{"name":"New Jersey Institute of Technology, US"}]},{"given":"Xinyuan","family":"Tao","sequence":"additional","affiliation":[{"name":"New Jersey Institute of Technology, US"}]},{"given":"Zuofeng","family":"Shang","sequence":"additional","affiliation":[{"name":"New Jersey Institute of Technology, US"}]},{"given":"Dantong","family":"Yu","sequence":"additional","affiliation":[{"name":"New Jersey Institute of Technology, US"}]}],"member":"320","published-online":{"date-parts":[[2022,10,26]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jfineco.2018.08.002"},{"key":"e_1_3_2_1_2_1","volume-title":"Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting. Advances in Neural Information Processing Systems 33","author":"Cao Defu","year":"2020","unstructured":"Defu Cao , Yujing Wang , Juanyong Duan , Ce Zhang , Xia Zhu , Congrui Huang , Yunhai Tong , Bixiong Xu , Jing Bai , Jie Tong , 2020. Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting. Advances in Neural Information Processing Systems 33 ( 2020 ). Defu Cao, Yujing Wang, Juanyong Duan, Ce Zhang, Xia Zhu, Congrui Huang, Yunhai Tong, Bixiong Xu, Jing Bai, Jie Tong, 2020. Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting. Advances in Neural Information Processing Systems 33 (2020)."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2020.12.068"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108218"},{"key":"e_1_3_2_1_5_1","unstructured":"Junyoung Chung Caglar Gulcehre KyungHyun Cho and Yoshua Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555(2014).  Junyoung Chung Caglar Gulcehre KyungHyun Cho and Yoshua Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555(2014)."},{"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. CoRR abs\/1606.09375(2016). arXiv:1606.09375http:\/\/arxiv.org\/abs\/1606.09375  Micha\u00ebl Defferrard Xavier Bresson and Pierre Vandergheynst. 2016. Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering. CoRR abs\/1606.09375(2016). arXiv:1606.09375http:\/\/arxiv.org\/abs\/1606.09375"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301890"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/0304-405X(93)90023-5"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"crossref","unstructured":"Shen Fang Qi Zhang Gaofeng Meng Shiming Xiang and Chunhong Pan. 2019. GSTNet: Global Spatial-Temporal Network for Traffic Flow Prediction.. In IJCAI. 2286\u20132293.  Shen Fang Qi Zhang Gaofeng Meng Shiming Xiang and Chunhong Pan. 2019. GSTNet: Global Spatial-Temporal Network for Traffic Flow Prediction.. In IJCAI. 2286\u20132293.","DOI":"10.24963\/ijcai.2019\/317"},{"key":"e_1_3_2_1_10_1","volume-title":"SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks. CoRR abs\/2102.05034(2021). arXiv:2102.05034https:\/\/arxiv.org\/abs\/2102.05034","author":"Fatemi Bahare","year":"2021","unstructured":"Bahare Fatemi , Layla\u00a0El Asri , and Seyed\u00a0Mehran Kazemi . 2021 . SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks. CoRR abs\/2102.05034(2021). arXiv:2102.05034https:\/\/arxiv.org\/abs\/2102.05034 Bahare Fatemi, Layla\u00a0El Asri, and Seyed\u00a0Mehran Kazemi. 2021. SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks. CoRR abs\/2102.05034(2021). arXiv:2102.05034https:\/\/arxiv.org\/abs\/2102.05034"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3309547"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108119"},{"key":"e_1_3_2_1_13_1","volume-title":"International conference on artificial intelligence and statistics. PMLR, 1651\u20131661","author":"Fortuin Vincent","year":"2020","unstructured":"Vincent Fortuin , Dmitry Baranchuk , Gunnar R\u00e4tsch , and Stephan Mandt . 2020 . Gp-vae: Deep probabilistic time series imputation . In International conference on artificial intelligence and statistics. PMLR, 1651\u20131661 . Vincent Fortuin, Dmitry Baranchuk, Gunnar R\u00e4tsch, and Stephan Mandt. 2020. Gp-vae: Deep probabilistic time series imputation. In International conference on artificial intelligence and statistics. PMLR, 1651\u20131661."},{"key":"e_1_3_2_1_14_1","volume-title":"Learning Precise Timing with Lstm Recurrent Networks. J. Mach. Learn. Res. 3, null (mar","author":"Gers A.","year":"2003","unstructured":"Felix\u00a0 A. Gers , Nicol\u00a0 N. Schraudolph , and J\u00fcrgen Schmidhuber . 2003. Learning Precise Timing with Lstm Recurrent Networks. J. Mach. Learn. Res. 3, null (mar 2003 ), 115\u2013143. https:\/\/doi.org\/10.1162\/153244303768966139 10.1162\/153244303768966139 Felix\u00a0A. Gers, Nicol\u00a0N. Schraudolph, and J\u00fcrgen Schmidhuber. 2003. Learning Precise Timing with Lstm Recurrent Networks. J. Mach. Learn. Res. 3, null (mar 2003), 115\u2013143. https:\/\/doi.org\/10.1162\/153244303768966139"},{"key":"e_1_3_2_1_15_1","volume-title":"Learning Precise Timing with Lstm Recurrent Networks. J. Mach. Learn. Res. 3, null (mar","author":"Gers A.","year":"2003","unstructured":"Felix\u00a0 A. Gers , Nicol\u00a0 N. Schraudolph , and J\u00fcrgen Schmidhuber . 2003. Learning Precise Timing with Lstm Recurrent Networks. J. Mach. Learn. Res. 3, null (mar 2003 ), 115\u2013143. https:\/\/doi.org\/10.1162\/153244303768966139 10.1162\/153244303768966139 Felix\u00a0A. Gers, Nicol\u00a0N. Schraudolph, and J\u00fcrgen Schmidhuber. 2003. Learning Precise Timing with Lstm Recurrent Networks. J. Mach. Learn. Res. 3, null (mar 2003), 115\u2013143. https:\/\/doi.org\/10.1162\/153244303768966139"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1093\/rfs\/hhaa009"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301922"},{"key":"e_1_3_2_1_18_1","volume-title":"Inductive representation learning on large graphs. Advances in neural information processing systems","author":"Hamilton Will","year":"2017","unstructured":"Will Hamilton , Zhitao Ying , and Jure Leskovec . 2017. Inductive representation learning on large graphs. Advances in neural information processing systems ( 2017 ). Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. Advances in neural information processing systems (2017)."},{"key":"e_1_3_2_1_19_1","volume-title":"Hats: A hierarchical graph attention network for stock movement prediction. arXiv preprint arXiv:1908.07999(2019).","author":"Kim Raehyun","year":"2019","unstructured":"Raehyun Kim , Chan\u00a0Ho So , Minbyul Jeong , Sanghoon Lee , Jinkyu Kim , and Jaewoo Kang . 2019 . Hats: A hierarchical graph attention network for stock movement prediction. arXiv preprint arXiv:1908.07999(2019). Raehyun Kim, Chan\u00a0Ho So, Minbyul Jeong, Sanghoon Lee, Jinkyu Kim, and Jaewoo Kang. 2019. Hats: A hierarchical graph attention network for stock movement prediction. arXiv preprint arXiv:1908.07999(2019)."},{"key":"e_1_3_2_1_20_1","volume-title":"Kipf and Max Welling","author":"N.","year":"2017","unstructured":"Thomas\u00a0 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. Thomas\u00a0N. 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."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/626"},{"key":"e_1_3_2_1_22_1","volume-title":"Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting. In International Conference on Learning Representations.","author":"Li Yaguang","year":"2018","unstructured":"Yaguang Li , Rose Yu , Cyrus Shahabi , and Yan Liu . 2018 . Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting. In International Conference on Learning Representations. Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu. 2018. Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330881"},{"key":"e_1_3_2_1_24_1","unstructured":"Jiawei Ma Zheng Shou Alireza Zareian Hassan Mansour Anthony Vetro and Shih-Fu Chang. 2019. CDSA: cross-dimensional self-attention for multivariate geo-tagged time series imputation. arXiv preprint arXiv:1905.09904(2019).  Jiawei Ma Zheng Shou Alireza Zareian Hassan Mansour Anthony Vetro and Shih-Fu Chang. 2019. CDSA: cross-dimensional self-attention for multivariate geo-tagged time series imputation. arXiv preprint arXiv:1905.09904(2019)."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5440"},{"key":"e_1_3_2_1_26_1","volume-title":"Nonlinear Tensor Completion Using Domain Knowledge: An Application in Analysts\u2019 Earnings Forecast. In 2020 International Conference on Data Mining Workshops (ICDMW). IEEE, 377\u2013384","author":"Uddin Ajim","year":"2020","unstructured":"Ajim Uddin , Xinyuan Tao , Chia-Ching Chou , and Dantong Yu . 2020 . Nonlinear Tensor Completion Using Domain Knowledge: An Application in Analysts\u2019 Earnings Forecast. In 2020 International Conference on Data Mining Workshops (ICDMW). IEEE, 377\u2013384 . Ajim Uddin, Xinyuan Tao, Chia-Ching Chou, and Dantong Yu. 2020. Nonlinear Tensor Completion Using Domain Knowledge: An Application in Analysts\u2019 Earnings Forecast. In 2020 International Conference on Data Mining Workshops (ICDMW). IEEE, 377\u2013384."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1080\/14697688.2021.1963825"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482413"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbef.2020.100353"},{"key":"#cr-split#-e_1_3_2_1_30_1.1","unstructured":"Petar Veli\u010dkovi\u0107 Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Li\u00f2 and Yoshua Bengio. 2017. Graph Attention Networks. https:\/\/doi.org\/10.48550\/ARXIV.1710.10903 10.48550\/ARXIV.1710.10903"},{"key":"#cr-split#-e_1_3_2_1_30_1.2","unstructured":"Petar Veli\u010dkovi\u0107 Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Li\u00f2 and Yoshua Bengio. 2017. Graph Attention Networks. https:\/\/doi.org\/10.48550\/ARXIV.1710.10903"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611976700.79"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2012.2203122"},{"key":"e_1_3_2_1_33_1","unstructured":"Jiaxuan You Xiaobai Ma Daisy\u00a0Yi Ding Mykel\u00a0J. Kochenderfer and Jure Leskovec. 2020. Handling Missing Data with Graph Representation Learning. CoRR abs\/2010.16418(2020). arXiv:2010.16418https:\/\/arxiv.org\/abs\/2010.16418  Jiaxuan You Xiaobai Ma Daisy\u00a0Yi Ding Mykel\u00a0J. Kochenderfer and Jure Leskovec. 2020. Handling Missing Data with Graph Representation Learning. CoRR abs\/2010.16418(2020). arXiv:2010.16418https:\/\/arxiv.org\/abs\/2010.16418"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/505"}],"event":{"name":"ICAIF '22: 3rd ACM International Conference on AI in Finance","location":"New York NY USA","acronym":"ICAIF '22","sponsor":["ACM Association for Computing Machinery"]},"container-title":["Proceedings of the Third ACM International Conference on AI in Finance"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3533271.3561751","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3533271.3561751","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:00:39Z","timestamp":1750186839000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3533271.3561751"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,26]]},"references-count":35,"alternative-id":["10.1145\/3533271.3561751","10.1145\/3533271"],"URL":"https:\/\/doi.org\/10.1145\/3533271.3561751","relation":{},"subject":[],"published":{"date-parts":[[2022,10,26]]},"assertion":[{"value":"2022-10-26","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}