Jian Tang (唐建）

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Meng QuQuebec AI Institute (Mila)Verified email at umontreal.ca
Ming Zhang, 张铭Professor,Peking UniversityVerified email at pku.edu.cn
Qiaozhu MeiProfessor, University of MichiganVerified email at umich.edu
Chence ShiQuebec AI Institute (Mila)Verified email at umontreal.ca
Yoshua BengioProfessor of computer science, University of Montreal, Mila, IVADO, CIFARVerified email at umontreal.ca
Minkai XuStanford UniversityVerified email at stanford.edu
Zhaocheng ZhuMila - Quebec AI InstituteVerified email at umontreal.ca
Zhi-Hong DengSchool of Electronics Engineering and Computer Science, Peking UniversityVerified email at pku.edu.cn
Zhiyuan LiuAssociate Professor, Tsinghua UniversityVerified email at tsinghua.edu.cn
Weiping Song (宋卫平)Peking UniversityVerified email at pku.edu.cn
Shengchao(Chao) LiuMila-UdeMVerified email at umontreal.ca
Andreea DeacMila, Université de MontréalVerified email at mila.quebec
Feixiong ChengCleveland ClinicVerified email at ccf.org
XuanLong NguyenUniversity of MichiganVerified email at umich.edu
Laurent CharlinAssociate Professor, HEC Montréal & Mila, Canada CIFAR AI ChairVerified email at hec.ca
P. Lió, Pietro Lio, P. Lio, Pietro Liò, Pie...University of CambridgeVerified email at cam.ac.uk
Connor W. ColeyDepartment of Chemical Engineering, MITVerified email at mit.edu
Xuanzhe LiuPeking University | ACM Distinguished ScientistVerified email at pku.edu.cn
Bolanle OjokohDepartment of Information Systems, Federal University of Technology, Akure, NigeriaVerified email at futa.edu.ng
Rafael Gómez-BombarelliAssistant Professor, MIT DMSEVerified email at mit.edu

Jian Tang (唐建）

Associate Professor, Mila-Quebec AI Institute, HEC Montréal, Canada CIFAR AI Chair

Verified email at hec.ca - Homepage

Geometric deep learning AI for molecule/protein design knowledge graphs deep generative models


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Line: Large-scale information network embedding J Tang, M Qu, M Wang, M Zhang, J Yan, Q Mei Proceedings of the 24th international conference on world wide web, 1067-1077, 2015	5064	2015
Rotate: Knowledge graph embedding by relational rotation in complex space Z Sun, ZH Deng, JY Nie, J Tang ICLR 2019, 2019	1255	2019
Pte: Predictive text embedding through large-scale heterogeneous text networks J Tang, M Qu, Q Mei Proceedings of the 21th ACM SIGKDD international conference on knowledge …, 2015	803	2015
InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization FY Sun, J Hoffmann, V Verma, J Tang ICLR 2020 (Spotlight), 2020	446	2020
Visualizing large-scale and high-dimensional data J Tang, J Liu, M Zhang, Q Mei Proceedings of the 25th international conference on world wide web, 287-297, 2016	406	2016
AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks W Song, C Shi, Z Xiao, Z Duan, Y Xu, M Zhang, J Tang CIKM 2019, 2019	392	2019
Deepinf: Social influence prediction with deep learning J Qiu, J Tang, H Ma, Y Dong, K Wang, J Tang Proceedings of the 24th ACM SIGKDD international conference on knowledge …, 2018	380	2018
Artificial intelligence in COVID-19 drug repurposing Y Zhou, F Wang, J Tang, R Nussinov, F Cheng The Lancet Digital Health 2 (12), e667-e676, 2020	334	2020
Understanding the limiting factors of topic modeling via posterior contraction analysis J Tang, Z Meng, X Nguyen, Q Mei, M Zhang International conference on machine learning, 190-198, 2014	323	2014
Session-based social recommendation via dynamic graph attention networks W Song, Z Xiao, Y Wang, L Charlin, M Zhang, J Tang Proceedings of the Twelfth ACM international conference on web search and …, 2019	286	2019
KEPLER: A unified model for knowledge embedding and pre-trained language representation X Wang, T Gao, Z Zhu, Z Zhang, Z Liu, J Li, J Tang Transactions of the Association for Computational Linguistics 9, 176-194, 2021	272	2021
GMNN: Graph Markov Neural Networks M Qu, Y Bengio, J Tang ICML 2019, 2019	244	2019
Adversarial network embedding Q Dai, Q Li, J Tang, D Wang Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018	223	2018
GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation C Shi, M Xu, Z Zhu, W Zhang, M Zhang, J Tang ICLR 2020, 2020	202	2020
An attention-based collaboration framework for multi-view network representation learning M Qu, J Tang, J Shang, X Ren, M Zhang, J Han Proceedings of the 2017 ACM on Conference on Information and Knowledge …, 2017	149	2017
Probabilistic Logic Neural Networks for Reasoning M Qu, J Tang NeurIPS'19, 2019	121	2019
Utilizing graph machine learning within drug discovery and development T Gaudelet, B Day, AR Jamasb, J Soman, C Regep, G Liu, JBR Hayter, ... Briefings in bioinformatics 22 (6), bbab159, 2021	104	2021
GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding Z Zhu, S Xu, M Qu, J Tang The World Wide Web Conference, 2494-2504, 2019	95	2019
Graphmix: Regularized training of graph neural networks for semi-supervised learning V Verma, M Qu, A Lamb, Y Bengio, J Kannala, J Tang AAAI'21, 2021	92*	2021
A Graph to Graphs Framework for Retrosynthesis Prediction C Shi, M Xu, H Guo, M Zhang, J Tang ICML'20, 2020	83	2020

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