{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T16:05:02Z","timestamp":1775837102959,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":40,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,8,14]],"date-time":"2021-08-14T00:00:00Z","timestamp":1628899200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-sa\/4.0\/"}],"funder":[{"name":"NSF (National Science Foundation) CAREER Award","award":["1741634"],"award-info":[{"award-number":["1741634"]}]},{"name":"Amazon Research Awards"},{"DOI":"10.13039\/100000002","name":"NIH (National Institutes of Health)","doi-asserted-by":"publisher","award":["R31-HL 135772"],"award-info":[{"award-number":["R31-HL 135772"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"name":"NSF (National Science Foundation) IIS-","award":["2031187"],"award-info":[{"award-number":["2031187"]}]},{"name":"NSF (National Science Foundation) DGE-","award":["1829071"],"award-info":[{"award-number":["1829071"]}]},{"name":"Okawa Foundation Grant"},{"name":"DARPA","award":["HR00112090027"],"award-info":[{"award-number":["HR00112090027"]}]},{"name":"NSF (National Science Foundation) ?","award":["1705169"],"award-info":[{"award-number":["1705169"]}]},{"DOI":"10.13039\/100000001","name":"NSF (National Science Foundation)","doi-asserted-by":"publisher","award":["1937599"],"award-info":[{"award-number":["1937599"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"name":"NIH (National Institutes of Health) R01","award":["EB027650"],"award-info":[{"award-number":["EB027650"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,8,14]]},"DOI":"10.1145\/3447548.3467385","type":"proceedings-article","created":{"date-parts":[[2021,8,13]],"date-time":"2021-08-13T18:33:08Z","timestamp":1628879588000},"page":"705-715","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":48,"title":["Coupled Graph ODE for Learning Interacting System Dynamics"],"prefix":"10.1145","author":[{"given":"Zijie","family":"Huang","sequence":"first","affiliation":[{"name":"University of California, Los Angeles, Los Angeles, CA, USA"}]},{"given":"Yizhou","family":"Sun","sequence":"additional","affiliation":[{"name":"University of California, Los Angeles, Los Angeles, CA, USA"}]},{"given":"Wei","family":"Wang","sequence":"additional","affiliation":[{"name":"University of California, Los Angeles, Los Angeles, CA, USA"}]}],"member":"320","published-online":{"date-parts":[[2021,8,14]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3289600.3290967"},{"key":"e_1_3_2_2_2_1","volume-title":"Relations and Physics. In NIPS'16","author":"Battaglia Peter","unstructured":"Peter Battaglia , Razvan Pascanu , Matthew Lai , Danilo Jimenez Rezende , and koray kavukcuoglu. 2016. Interaction Networks for Learning about Objects , Relations and Physics. In NIPS'16 . Peter Battaglia, Razvan Pascanu, Matthew Lai, Danilo Jimenez Rezende, and koray kavukcuoglu. 2016. Interaction Networks for Learning about Objects, Relations and Physics. In NIPS'16."},{"key":"e_1_3_2_2_3_1","unstructured":"Maximilian Behr Peter Benner and Jan Heiland. 2019. Solution formulas for differential Sylvester and Lyapunov equations. In Calcolo.  Maximilian Behr Peter Benner and Jan Heiland. 2019. Solution formulas for differential Sylvester and Lyapunov equations. In Calcolo."},{"key":"e_1_3_2_2_4_1","volume-title":"ICLR'16","author":"Chang Michael B","year":"2016","unstructured":"Michael B Chang , Tomer Ullman , Antonio Torralba , and Joshua B Tenenbaum . 2016 . A Compositional Object-Based Approach to Learning Physical Dynamics . ICLR'16 (2016). Michael B Chang, Tomer Ullman, Antonio Torralba, and Joshua B Tenenbaum. 2016. A Compositional Object-Based Approach to Learning Physical Dynamics. ICLR'16 (2016)."},{"key":"e_1_3_2_2_5_1","volume-title":"Jaline Gerardin, Beth Redbird, David Grusky, and Jure Leskovec.","author":"Chang Serina","year":"2021","unstructured":"Serina Chang , Emma Pierson , Pang Wei Koh , Jaline Gerardin, Beth Redbird, David Grusky, and Jure Leskovec. 2021 . Mobility network models of COVID-19 explain inequities and inform reopening. In Nature . Serina Chang, Emma Pierson, Pang Wei Koh, Jaline Gerardin, Beth Redbird, David Grusky, and Jure Leskovec. 2021. Mobility network models of COVID-19 explain inequities and inform reopening. In Nature."},{"key":"e_1_3_2_2_6_1","unstructured":"Zhengping Che Sanjay Purushotham Kyunghyun Cho David Sontag and Yan. Liu. 2018. Recurrent Neural Networks for Multivariate Time Series with Missing Values. In Scientific Reports.  Zhengping Che Sanjay Purushotham Kyunghyun Cho David Sontag and Yan. Liu. 2018. Recurrent Neural Networks for Multivariate Time Series with Missing Values. In Scientific Reports."},{"key":"e_1_3_2_2_7_1","volume-title":"Neural Ordinary Differential Equations. In NIPS'18","author":"Chen Ricky T. Q.","year":"2018","unstructured":"Ricky T. Q. Chen , Yulia Rubanova , Jesse Bettencourt , and David K Duvenaud . 2018 . Neural Ordinary Differential Equations. In NIPS'18 . Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud. 2018. Neural Ordinary Differential Equations. In NIPS'18."},{"key":"e_1_3_2_2_8_1","unstructured":"Miles Cranmer Alvaro Sanchez-Gonzalez Peter Battaglia Rui Xu Kyle Cranme David Spergel and Shirley Ho. 2020. Discovering Symbolic Models from Deep Learning with Inductive Biases. In Neurips'20.  Miles Cranmer Alvaro Sanchez-Gonzalez Peter Battaglia Rui Xu Kyle Cranme David Spergel and Shirley Ho. 2020. Discovering Symbolic Models from Deep Learning with Inductive Biases. In Neurips'20."},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"crossref","unstructured":"Ensheng Dong Hongru Du and Lauren Gardner. 2020. An interactive web-based dashboard to track COVID-19 in real time. In The Lancet Infectious Diseases.  Ensheng Dong Hongru Du and Lauren Gardner. 2020. An interactive web-based dashboard to track COVID-19 in real time. In The Lancet Infectious Diseases.","DOI":"10.1016\/S1473-3099(20)30120-1"},{"key":"e_1_3_2_2_10_1","volume":"198","author":"Dormand J.R.","unstructured":"J.R. Dormand and P. J. Prince. 198 0. A family of embedded Runge-Kutta formulae. In Journal of Computational and Applied Mathematics. J.R. Dormand and P.J. Prince. 1980. A family of embedded Runge-Kutta formulae. In Journal of Computational and Applied Mathematics.","journal-title":"J. Prince."},{"key":"e_1_3_2_2_11_1","volume-title":"The Co-Evolution Model for Social Network Evolving and Opinion Migration. In KDD'17","author":"Gu Yupeng","year":"2017","unstructured":"Yupeng Gu , Yizhou Sun , and Jianxi Gao . 2017 . The Co-Evolution Model for Social Network Evolving and Opinion Migration. In KDD'17 . Yupeng Gu, Yizhou Sun, and Jianxi Gao. 2017. The Co-Evolution Model for Social Network Evolving and Opinion Migration. In KDD'17."},{"key":"e_1_3_2_2_12_1","unstructured":"Ehsan Hajiramezanali Arman Hasanzadeh Krishna Narayanan Nick Duffield Mingyuan Zhou and Xiaoning Qian. 2019. Variational graph recurrent neural networks. In Neurips'19.  Ehsan Hajiramezanali Arman Hasanzadeh Krishna Narayanan Nick Duffield Mingyuan Zhou and Xiaoning Qian. 2019. Variational graph recurrent neural networks. In Neurips'19."},{"key":"e_1_3_2_2_13_1","unstructured":"IHME COVID-19 health service utilization forecasting team and Christopher JL Murray. 2020. Forecasting COVID-19 impact on hospital bed-days ICU-days ventilator-days and deaths by US state in the next 4 months. In medRxiv preprint :2020.03.27.20043752.  IHME COVID-19 health service utilization forecasting team and Christopher JL Murray. 2020. Forecasting COVID-19 impact on hospital bed-days ICU-days ventilator-days and deaths by US state in the next 4 months. In medRxiv preprint :2020.03.27.20043752."},{"key":"e_1_3_2_2_14_1","volume-title":"ICLR'19","author":"Hendrycks Dan","year":"2019","unstructured":"Dan Hendrycks and Kevin Gimpel . 2019 . Decoupled weighted decay regularization . ICLR'19 (2019). Dan Hendrycks and Kevin Gimpel. 2019. Decoupled weighted decay regularization. ICLR'19 (2019)."},{"key":"e_1_3_2_2_15_1","volume-title":"Gaussian Error Linear Units (GELUs). arXiv","author":"Hendrycks Dan","year":"2020","unstructured":"Dan Hendrycks and Kevin Gimpel . 2020. Gaussian Error Linear Units (GELUs). arXiv ( 2020 ). Dan Hendrycks and Kevin Gimpel. 2020. Gaussian Error Linear Units (GELUs). arXiv (2020)."},{"key":"e_1_3_2_2_16_1","series-title":"SIAM review.","volume-title":"The mathematics of infectious diseases","author":"Hethcote H. W","unstructured":"H. W Hethcote . 2000. The mathematics of infectious diseases . In SIAM review. H. W Hethcote. 2000. The mathematics of infectious diseases. In SIAM review."},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380027"},{"key":"e_1_3_2_2_18_1","unstructured":"Zijie Huang Yizhou Sun and Wei Wang. 2020. Learning Continuous System Dynamics from Irregularly-Sampled Partial Observations. In Neurips'20.  Zijie Huang Yizhou Sun and Wei Wang. 2020. Learning Continuous System Dynamics from Irregularly-Sampled Partial Observations. In Neurips'20."},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380182"},{"key":"e_1_3_2_2_20_1","volume-title":"ICLR'14","author":"Diederik","unstructured":"Diederik P. Kingma and Max Welling. 2014. Auto-Encoding Variational Bayes .. In ICLR'14 . Diederik P. Kingma and Max Welling. 2014. Auto-Encoding Variational Bayes.. In ICLR'14."},{"key":"e_1_3_2_2_21_1","volume-title":"Neural Relational Inference for Interacting Systems. arXiv preprint arXiv:1802.04687","author":"Kipf Thomas","year":"2018","unstructured":"Thomas Kipf , Ethan Fetaya , Kuan-Chieh Wang , Max Welling , and Richard Zemel . 2018. Neural Relational Inference for Interacting Systems. arXiv preprint arXiv:1802.04687 ( 2018 ). Thomas Kipf, Ethan Fetaya, Kuan-Chieh Wang, Max Welling, and Richard Zemel. 2018. Neural Relational Inference for Interacting Systems. arXiv preprint arXiv:1802.04687 (2018)."},{"key":"e_1_3_2_2_22_1","volume-title":"Semi-Supervised Classification with Graph Convolutional Networks. In ICLR'17","author":"Thomas","unstructured":"Thomas N. Kipf and Max Welling. 2017 . Semi-Supervised Classification with Graph Convolutional Networks. In ICLR'17 . Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In ICLR'17."},{"key":"e_1_3_2_2_23_1","volume-title":"CIKM'17","author":"Jundong","year":"2017","unstructured":"Jundong li, Harsh Dani , Xia Hu , Jiliang Tang , Yi Chang , and Huan Liu . 2017 . Attributed Network Embedding for Learning in a Dynamic Environment . In CIKM'17 . Jundong li, Harsh Dani, Xia Hu, Jiliang Tang, Yi Chang, and Huan Liu. 2017. Attributed Network Embedding for Learning in a Dynamic Environment. In CIKM'17."},{"key":"e_1_3_2_2_24_1","unstructured":"Yunzhu Li Jiajun Wu Jun-Yan Zhu Joshua B Tenenbaum Antonio Torralba and Russ Tedrake. 2019. Propagation Networks for Model-Based Control Under Partial Observation. In ICRA.  Yunzhu Li Jiajun Wu Jun-Yan Zhu Joshua B Tenenbaum Antonio Torralba and Russ Tedrake. 2019. Propagation Networks for Model-Based Control Under Partial Observation. In ICRA."},{"key":"e_1_3_2_2_25_1","volume-title":"Graph Neural Ordinary Differential Equations. arXiv","author":"Poli Michael","year":"2019","unstructured":"Michael Poli , Stefano Massaroli , Junyoung Park , Atsushi Yamashita , Hajime Asama , and Jinkyoo Park . 2019. Graph Neural Ordinary Differential Equations. arXiv ( 2019 ). Michael Poli, Stefano Massaroli, Junyoung Park, Atsushi Yamashita, Hajime Asama, and Jinkyoo Park. 2019. Graph Neural Ordinary Differential Equations. arXiv (2019)."},{"key":"e_1_3_2_2_26_1","unstructured":"Yulia Rubanova Ricky T. Q. Chen and David K Duvenaud. 2019. Latent Ordinary Differential Equations for Irregularly-Sampled Time Series. In Neurips'19.  Yulia Rubanova Ricky T. Q. Chen and David K Duvenaud. 2019. Latent Ordinary Differential Equations for Irregularly-Sampled Time Series. In Neurips'19."},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371845"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"crossref","unstructured":"Michael Schober Simo Sarkka and Philipp Hennig. 2019. A probabilistic model for the numerical solution \u00a8 of initial value problems. In Statistics and Computing.  Michael Schober Simo Sarkka and Philipp Hennig. 2019. A probabilistic model for the numerical solution \u00a8 of initial value problems. In Statistics and Computing.","DOI":"10.1007\/s11222-017-9798-7"},{"key":"e_1_3_2_2_29_1","volume-title":"Long Short-term Memory. Neural computation","author":"Sepp Hochreiter","year":"1997","unstructured":"Hochreiter Sepp and Schmidhuber J\u00fcrgen . 1997. Long Short-term Memory. Neural computation ( 1997 ). Hochreiter Sepp and Schmidhuber J\u00fcrgen. 1997. Long Short-term Memory. Neural computation (1997)."},{"key":"e_1_3_2_2_30_1","volume-title":"NIPS'17","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani , Noam Shazeer , Niki Parmar , Jakob Uszkoreit , Llion Jones , Aidan N Gomez , \u0141 ukasz Kaiser, and Illia Polosukhin . 2017 . Attention is All you Need . In NIPS'17 . Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, \u0141 ukasz Kaiser, and Illia Polosukhin. 2017. Attention is All you Need. In NIPS'17."},{"key":"e_1_3_2_2_31_1","volume-title":"Graph Attention Networks. ICLR'18","author":"Petar Velivc","year":"2018","unstructured":"Petar Velivc kovi\u0107, Guillem Cucurull , Arantxa Casanova , Adriana Romero , Pietro Li\u00f2 , and Yoshua Bengio . 2018 . Graph Attention Networks. ICLR'18 (2018). Petar Velivc kovi\u0107, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Li\u00f2, and Yoshua Bengio. 2018. Graph Attention Networks. ICLR'18 (2018)."},{"key":"e_1_3_2_2_32_1","volume-title":"Author Name Disambiguation on Heterogeneous Information Network with Adversarial Representation Learning. In AAAI '20","author":"Wang Haiwen","year":"2020","unstructured":"Haiwen Wang , Ruijie Wang , Chuan Wen , Shuhao Li , Yuting Jia , Weinan Zhang , and Xinbing Wang . 2020 a . Author Name Disambiguation on Heterogeneous Information Network with Adversarial Representation Learning. In AAAI '20 . Haiwen Wang, Ruijie Wang, Chuan Wen, Shuhao Li, Yuting Jia, Weinan Zhang, and Xinbing Wang. 2020 a. Author Name Disambiguation on Heterogeneous Information Network with Adversarial Representation Learning. In AAAI '20."},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"crossref","unstructured":"Qinxia Wang Shanghong Xie Yuanjia Wang and Zeng Donglin. 2020 b. Survival-Convolution Models for Predicting COVID-19 Cases and Assessing Effects of Mitigation Strategies. In Frontiers in Public Health.  Qinxia Wang Shanghong Xie Yuanjia Wang and Zeng Donglin. 2020 b. Survival-Convolution Models for Predicting COVID-19 Cases and Assessing Effects of Mitigation Strategies. In Frontiers in Public Health.","DOI":"10.1101\/2020.04.16.20067306"},{"key":"e_1_3_2_2_34_1","volume-title":"DyDiff-VAE: A Dynamic Variational Framework for Information Diffusion Prediction. In SIGIR'21","author":"Wang Ruijie","year":"2021","unstructured":"Ruijie Wang , Zijie Huang , Shengzhong Liu , Huajie Shao , Dongxin Liu , Jinyang Li , Tianshi Wang , Dachun Sun , Shuochao Yao , and Tarek Abdelzaher . 2021 . DyDiff-VAE: A Dynamic Variational Framework for Information Diffusion Prediction. In SIGIR'21 . Ruijie Wang, Zijie Huang, Shengzhong Liu, Huajie Shao, Dongxin Liu, Jinyang Li, Tianshi Wang, Dachun Sun, Shuochao Yao, and Tarek Abdelzaher. 2021. DyDiff-VAE: A Dynamic Variational Framework for Information Diffusion Prediction. In SIGIR'21."},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3269252"},{"key":"e_1_3_2_2_36_1","volume-title":"Lauren Ancel Meyers, and James Scott","author":"Woody Spencer","year":"2020","unstructured":"Spencer Woody , Mauricio Tec , Maytal Dahan , Kelly Gaither , Michael Lachmann , Spencer J. Fox , Lauren Ancel Meyers, and James Scott . 2020 . Projections for first-wave COVID-19 deaths across the U.S. using social-distancing measures derived from mobile phones. Spencer Woody, Mauricio Tec, Maytal Dahan, Kelly Gaither, Michael Lachmann, Spencer J. Fox, Lauren Ancel Meyers, and James Scott. 2020. Projections for first-wave COVID-19 deaths across the U.S. using social-distancing measures derived from mobile phones."},{"key":"e_1_3_2_2_37_1","volume-title":"Continuous Graph Neural Networks. In ICML'20","author":"Xhonneux Louis-Pascal","year":"2020","unstructured":"Louis-Pascal Xhonneux , Meng Qu , and Jian Tang . 2020 . Continuous Graph Neural Networks. In ICML'20 . Louis-Pascal Xhonneux, Meng Qu, and Jian Tang. 2020. Continuous Graph Neural Networks. In ICML'20."},{"key":"e_1_3_2_2_38_1","volume-title":"ICLR'19","author":"Xu Keyulu","year":"2019","unstructured":"Keyulu Xu , Weihua Hu , Jure Leskovec , and Stefanie Jegelka . 2019 . How Powerful are Graph Neural Networks? . In ICLR'19 . Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019. How Powerful are Graph Neural Networks?. In ICLR'19."},{"key":"e_1_3_2_2_39_1","volume-title":"Analyzing the Design Space of Re-opening Policies and COVID-19 Outcomes in the US. arxiv","author":"Yang Chaoqi","year":"2005","unstructured":"Chaoqi Yang , Ruijie Wang , Fangwei Gao , Dachun Sun , Jiawei Tang , and Tarek Abdelzaher . 2020. Analyzing the Design Space of Re-opening Policies and COVID-19 Outcomes in the US. arxiv : 2005 .00112 Chaoqi Yang, Ruijie Wang, Fangwei Gao, Dachun Sun, Jiawei Tang, and Tarek Abdelzaher. 2020. Analyzing the Design Space of Re-opening Policies and COVID-19 Outcomes in the US. arxiv: 2005.00112"},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"crossref","unstructured":"Difan Zou Lingxiao Wang Pan Xu Jinghui Chen Weitong Zhang and Quanquan Gu. 2020. Epidemic Model Guided Machine Learning for COVID-19 Forecasts in the United States. In medRxiv preprint :2020.05.24.20111989.  Difan Zou Lingxiao Wang Pan Xu Jinghui Chen Weitong Zhang and Quanquan Gu. 2020. Epidemic Model Guided Machine Learning for COVID-19 Forecasts in the United States. In medRxiv preprint :2020.05.24.20111989.","DOI":"10.1101\/2020.05.24.20111989"}],"event":{"name":"KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Virtual Event Singapore","acronym":"KDD '21","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3447548.3467385","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/abs\/10.1145\/3447548.3467385","content-type":"text\/html","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3447548.3467385","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3447548.3467385","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:18:23Z","timestamp":1750191503000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3447548.3467385"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,14]]},"references-count":40,"alternative-id":["10.1145\/3447548.3467385","10.1145\/3447548"],"URL":"https:\/\/doi.org\/10.1145\/3447548.3467385","relation":{},"subject":[],"published":{"date-parts":[[2021,8,14]]},"assertion":[{"value":"2021-08-14","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}