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Since the onset of the COVID-19 pandemic, there has been a growing interest in studying epidemiological models.
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In Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character 115.772
“A Contribution to the Mathematical Theory of Epidemics” · 1927
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“A contribution to the mathematical theory of epidemics”
William Kermack and Anderson McKendrick · 1927
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“Plagues and Peoples. By William H. McNeill. Pp. 369. (Basil Blackwell, Oxford, 1977.)”
L.. Bruce-Chwatt · 1977
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“The Society for Epidemiologic Research and the Future of Epidemiology”
Milton Terris · 1993
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“Maximizing the spread of influence through a social network”
David Kempe, Jon Kleinberg and Éva Tardos · 2003
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“Finding Patient Zero: Learning Contagion Source with Graph Neural Networks”
Chintan Shah et al · 2006
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“Intellectual property rights: A critical history”
Christopher May and Susan Sell · 2006
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“Examining COVID-19 Forecasting Using Spatio-Temporal Graph Neural Networks”
Amol Kapoor et al · 2007
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“Disease Control”
Dean. Jamison · 2007
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“Reinforced Epidemic Control: Saving Both Lives and Economy”
Sirui Song et al · 2008
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“Mathematical Models of Infectious Disease Transmission”
Nicholas. Grassly and Christophe Fraser · 2008
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“Influenza”
Rafael Mikolajczyk et al · 2009
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“Multiscale Mobility Networks and the Spatial Spreading of Infectious Diseases”
Duygu Balcan et al · 2009
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“Emergence of Viral Diseases: Mathematical Modeling as a Tool for Infection Control, Policy and Decision Making”
Derrick Louz, Hans. Bergmans, Birgit. Loos and Rob. Hoeben · 2010
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“Controlling Graph Dynamics with Reinforcement Learning and Graph Neural Networks”
Eli. Meirom, Haggai Maron, Shie Mannor and Gal Chechik · 2010
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“The Construction of Next-Generation Matrices for Compartmental Epidemic Models”
O. Diekmann, J… Heesterbeek and M.. Roberts · 2010
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“Networks and the epidemiology of infectious disease”
Leon Danon et al · 2011
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“Ancient Epidemic Diseases in a New Light”
Barbara Bramanti · 2012
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“ Networks— An Introduction . Mark E. J. Newman. (2010, Oxford University Press.) $65.38, £35.96 (Hardcover), 772 Pages. ISBN-978-0-19-920665-0.”
Markus Brede · 2012
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“An enhanced beam search algorithm for the shortest common supersequence problem”
Sayyed Mousavi, Fateme Bahri and Farzaneh Tabataba · 2012
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“Forecasting Methods and Models of Disease Spread”
Mikhail Kondratyev · 2013
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“Another Defining Moment for Epidemiology”
Paul Fine · 2015
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“Deep residual learning for image recognition”
Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun · 2016
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“The TimeGeo modeling framework for urban mobility without travel surveys”
Shan Jiang et al · 2016
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“Convolutional neural networks on graphs with fast localized spectral filtering”
Michaël Defferrard, Xavier Bresson and Pierre Vandergheynst · 2016
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“Ethics and Security Aspects of Infectious Disease Control: Interdisciplinary Perspectives”
Michael. Selgelid · 2016
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“Toward fairness in data sharing”
International of Investigators · 2016
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“Semi-supervised classification with graph convolutional networks”
Thomas Kipf and Max Welling · 2017
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Petar Veličković et al · 2017
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“Ethics of Epidemics, Research and Surveillance: A WHO Workshop Report”
Karel Caals, Abha Saxena and Calvin-Loon Ho · 2017
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“Spatio-Temporal Optimization of Seasonal Vaccination Using a Metapopulation Model of Influenza”
Srinivasan Venkatramanan et al · 2017
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“Reproduction Numbers of Infectious Disease Models”
Pauline Van · 2017
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“Three faces of node importance in network epidemiology: Exact results for small graphs”
Petter Holme · 2017
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“What is epidemiology? Changing definitions of epidemiology 1978-2017”
Mathilde Frérot et al · 2018
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“Real-Time Forecasting of Infectious Disease Dynamics with a Stochastic Semi-Mechanistic Model”
Sebastian Funk et al · 2018
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“Deep learning for epidemiological predictions”
Yuexin Wu, Yiming Yang, Hiroshi Nishiura and Masaya Saitoh · 2018
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“SEDS: Expanding TEDS to Include Physical Structures”
Brian Dixon, George Lecakes, Paul. Moon and John Schmalzel · 2018
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“Neural ordinary differential equations”
Ricky Chen, Yulia Rubanova, Jesse Bettencourt and David Duvenaud · 2018
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“Graph neural ordinary differential equations”
Michael Poli et al · 2019
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“Gnnexplainer: Generating explanations for graph neural networks”
Zhitao Ying et al · 2019
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“Does the Inadequate Health Resources Aggravate Covid-19 Pandemic?”
Jayadevan Cm · 2020
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“A comprehensive survey on graph neural networks”
Zonghan Wu et al · 2020
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“Cola-GNN: Cross-location Attention Based Graph Neural Networks for Long-term ILI Prediction”
Songgaojun Deng et al · 2020
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“A compact review of molecular property prediction with graph neural networks”
Oliver Wieder et al · 2020
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“Modelling the Impact of Testing, Contact Tracing and Household Quarantine on Second Waves of COVID-19”
Alberto Aleta et al · 2020
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“Modelling Transmission and Control of the COVID-19 Pandemic in Australia”
Sheryl. Chang et al · 2020
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“Inferring Change Points in the Spread of COVID-19 Reveals the Effectiveness of Interventions”
Jonas Dehning et al · 2020
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“Monitoring Italian COVID-19 Spread by a Forced SEIRD Model”
Elena Loli and Fabiana Zama · 2020
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“Beyond ranking nodes: Predicting epidemic outbreak sizes by network centralities”
Doina Bucur and Petter Holme · 2020
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“Continuous graph neural networks”
Louis-Pascal Xhonneux, Meng Qu and Jian Tang · 2020
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“Graph structure learning for robust graph neural networks”
Wei Jin et al · 2020
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“Deep Learning applications for COVID-19”
Connor Shorten, Taghi Khoshgoftaar and Borko Furht · 2021
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“Deep learning for virus-spreading forecasting: A brief survey”
Federico Baldo et al · 2021
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“STAN: Spatio-Temporal Attention Network for Pandemic Prediction Using Real-World Evidence”
Junyi Gao et al · 2021
Cited alongside, same era.
“Countrywide Origin-Destination Matrix Prediction and Its Application for COVID-19”
Renhe Jiang et al · 2021
Cited alongside, same era.
“Source Detection on Networks Using Spatial Temporal Graph Convolutional Networks”
Hao Sha, Mohammad Al and George Mohler · 2021
Cited alongside, same era.
“Effective Vaccination Strategy Using Graph Neural Network Ansatz”
Bukyoung Jhun · 2021
Cited alongside, same era.
“HierST: A Unified Hierarchical Spatial-temporal Framework for COVID-19 Trend Forecasting”
Shun Zheng et al · 2021
Cited alongside, same era.
“Prediction of the Effects of Epidemic Spreading with Graph Neural Networks”
Sebastian Mežnar, Nada Lavrač and Blaž Škrlj · 2021
“Uplifting Message Passing Neural Network with Graph Original Information”
Xiao Liu, Lijun Zhang and Hui Guan · 2023
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“Contact Tracing and Epidemic Intervention via Deep Reinforcement Learning”
Tao Feng, Sirui Song, Tong Xia and Yong Li · 2023
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“Epidemiology-Aware Deep Learning for Infectious Disease Dynamics Prediction”
Mutong Liu, Yang Liu and Jiming Liu · 2023
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“Inferring Patient Zero on Temporal Networks via Graph Neural Networks”
Xiaolei Ru et al · 2023
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“EpiMob: Interactive Visual Analytics of Citywide Human Mobility Restrictions for Epidemic Control”
Chuang Yang et al · 2023
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“Devil in the Landscapes: Inferring Epidemic Exposure Risks from Street View Imagery”
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Cited alongside, same era.
“Deep Learning of Contagion Dynamics on Complex Networks”
Charles Murphy, Edward Laurence and Antoine Allard · 2021
Cited alongside, same era.
“An Epidemiological Neural Network Exploiting Dynamic Graph Structured Data Applied to the COVID-19 Outbreak”
Valerio La, Vincenzo Moscato, Marco Postiglione and Giancarlo Sperli · 2021
Cited alongside, same era.
“Transfer Graph Neural Networks for Pandemic Forecasting”
George Panagopoulos, Giannis Nikolentzos and Michalis Vazirgiannis · 2021
Cited alongside, same era.
“Into the Unobservables: A Multi-range Encoder-decoder Framework for COVID-19 Prediction”
Yue Cui et al · 2021
Cited alongside, same era.
“Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning”
Yixin Liu et al · 2021
Cited alongside, same era.
“Integrating LSTMs and GNNs for COVID-19 Forecasting”
Nathan Sesti, Juan Garau-Luis, Edward Crawley and Bruce Cameron · 2021
Cited alongside, same era.
Zhenyu Han et al · 2023
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“WDCIP: Spatio-Temporal AI-driven Disease Control Intelligent Platform for Combating COVID-19 Pandemic”
Siqi Wang et al · 2023
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“Contact Tracing and Epidemic Intervention via Deep Reinforcement Learning”
Tao Feng, Sirui Song, Tong Xia and Yong Li · 2023
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“Human Mobility Modeling during the COVID-19 Pandemic via Deep Graph Diffusion Infomax”
Yang Liu et al · 2023
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“Predicting Influenza with Pandemic-Awareness via Dynamic Virtual Graph Significance Networks”
Jie Zhang, Pengfei Zhou, Yijia Zheng and Hongyan Wu · 2023
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“Enhancing Spatial Spread Prediction of Infectious Diseases through Integrating Multi-scale Human Mobility Dynamics”
Yinzhou Tang, Huandong Wang and Yong Li · 2023
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“Graph Neural Network Modeling of Web Search Activity for Real-time Pandemic Forecasting”
Chen Lin et al · 2023
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“Predicting COVID-19 Pandemic by Spatio-Temporal Graph Neural Networks: A New Zealand’s Study”, 2023
Viet Nguyen, Truong Hy, Long Tran-Thanh and Nhung Nghiem · 2023
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“Dynamic Adaptive Spatio–Temporal Graph Network for COVID-19 Forecasting”
Xiaojun Pu et al · 2023
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“MSGNN: Multi-scale Spatio-temporal Graph Neural Network for Epidemic Forecasting”
Mingjie Qiu, Zhiyi Tan and Bing-kun Bao · 2023
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“Spatio-Temporal Graph Learning for Epidemic Prediction”
Shuo Yu et al · 2023
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“Metapopulation Graph Neural Networks: Deep Metapopulation Epidemic Modeling with Human Mobility”, 2023, pp. 453–468
Qi Cao et al · 2023
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“RESEAT: Recurrent Self-Attention Network for Multi-Regional Influenza Forecasting”
Jaeuk Moon, Seungwon Jung, Sungwoo Park and Eenjun Hwang · 2023
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“Inferring Patient Zero on Temporal Networks via Graph Neural Networks”
Xiaolei Ru et al · 2023
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“COVID-19 Infection Inference with Graph Neural Networks”
Kyungwoo Song et al · 2023
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“Detection of Patients at Risk of Enterobacteriaceae Infection Using Graph Neural Networks: A Retrospective Study”
Racha Gouareb et al · 2023
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“Spatio-Temporal Prediction in Epidemiology Using Graph Convolution Network”
S. Siji et al · 2023
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“Spatio-Temporal Graph Learning for Epidemic Prediction”
Shuo Yu et al · 2023
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“Predicting COVID-19 Pandemic by Spatio-Temporal Graph Neural Networks: A New Zealand’s Study”, 2023
Viet Nguyen, Truong Hy, Long Tran-Thanh and Nhung Nghiem · 2023
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“Forecasting Infections with Spatio-Temporal Graph Neural Networks: A Case Study of the Dutch SARS-CoV-2 Spread”
V. Croft, Senna… van Iersel and Cosimo Della · 2023
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“RESEAT: Recurrent Self-Attention Network for Multi-Regional Influenza Forecasting”
Jaeuk Moon, Seungwon Jung, Sungwoo Park and Eenjun Hwang · 2023
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“Metapopulation Graph Neural Networks: Deep Metapopulation Epidemic Modeling with Human Mobility”, 2023, pp. 453–468
Qi Cao et al · 2023
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“Enhancing Spatial Spread Prediction of Infectious Diseases through Integrating Multi-scale Human Mobility Dynamics”
Yinzhou Tang, Huandong Wang and Yong Li · 2023
Later among the works it cites.
“Epidemiology-Aware Deep Learning for Infectious Disease Dynamics Prediction”
Mutong Liu, Yang Liu and Jiming Liu · 2023
Later among the works it cites.
“Predicting Influenza with Pandemic-Awareness via Dynamic Virtual Graph Significance Networks”
Jie Zhang, Pengfei Zhou, Yijia Zheng and Hongyan Wu · 2023
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“A Graph Based Deep Learning Framework for Predicting Spatio-Temporal Vaccine Hesitancy”
Sifat Moon et al · 2023
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“DeepDynaForecast: Phylogenetic-informed Graph Deep Learning for Epidemic Transmission Dynamic Prediction”
Chaoyue Sun et al · 2023
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“Novel Graph Topology Learning for Spatio-Temporal Analysis of COVID-19 Spread”
Baoling Shan et al · 2023
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“Dynamic Adaptive Spatio–Temporal Graph Network for COVID-19 Forecasting”
Xiaojun Pu et al · 2023
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“Detection of Patients at Risk of Enterobacteriaceae Infection Using Graph Neural Networks: A Retrospective Study”
Racha Gouareb et al · 2023
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“COVID-19 Infection Inference with Graph Neural Networks”
Kyungwoo Song et al · 2023
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“Attention Is All You Need”
Ashish Vaswani et al · 2023
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“Spatio-Temporal Prediction in Epidemiology Using Graph Convolution Network”
S. Siji et al · 2023
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“A Graph Based Deep Learning Framework for Predicting Spatio-Temporal Vaccine Hesitancy”
Sifat Moon et al · 2023
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“Time Series Forecasting of COVID-19 Cases in Brazil with GNN and Mobility Networks”, 2023, pp. 361–375
Fernando Duarte et al · 2023
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“MepoGNN: Metapopulation Epidemic Forecasting with Graph Neural Networks”
Qi Cao et al · 2023
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“Devil in the Landscapes: Inferring Epidemic Exposure Risks from Street View Imagery”
Zhenyu Han et al · 2023
Later among the works it cites.
“WDCIP: Spatio-Temporal AI-driven Disease Control Intelligent Platform for Combating COVID-19 Pandemic”
Siqi Wang et al · 2023
Later among the works it cites.
“MSGNN: Multi-scale Spatio-temporal Graph Neural Network for Epidemic Forecasting”
Mingjie Qiu, Zhiyi Tan and Bing-kun Bao · 2023
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“Graph Neural Network Modeling of Web Search Activity for Real-time Pandemic Forecasting”
Chen Lin et al · 2023
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“Public Health Interventions in the Control of Emerging Diseases”
Himashree Bhattacharyya and Rashmi Agarwalla · 2023
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“Data-centric ai: Perspectives and challenges”
Daochen Zha et al · 2023
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“A Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark”
Wenke Huang et al · 2023
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“Federated Graph Semantic and Structural Learning”
Wenke Huang, Guancheng Wan, Mang Ye and Bo Du · 2023
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“EpiLearn: A Python Library for Machine Learning in Epidemic Modeling”
Zewen Liu et al · 2024
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“An Introduction to Transformers”
Richard. Turner · 2024
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“Globally Interpretable Graph Learning via Distribution Matching”
Yi Nian, Yurui Chang, Wei Jin and Lu Lin · 2024
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“Federated Graph Learning under Domain Shift with Generalizable Prototypes”
Guancheng Wan, Wenke Huang and Mang Ye · 2024
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“Fair allocation of scarce medical resources in the time of Covid-19”
Ezekiel Emanuel et al · 2055
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