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
Minkai XuStanford UniversityVerified email at cs.stanford.edu
Yoshua BengioProfessor of computer science, University of Montreal, Mila, IVADO, CIFARVerified email at umontreal.ca
Zhaocheng ZhuMila - Quebec AI InstituteVerified email at umontreal.ca
Shengchao LiuUC Berkeley & CaltechVerified email at berkeley.edu
Zhi-Hong DengSchool of Intelligence Science and Technology, 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
Andreea DeacIsomorphic LabsVerified email at google.com
Feixiong ChengCleveland ClinicVerified email at ccf.org
Laurent CharlinAssociate Professor, HEC Montréal & Mila, Canada CIFAR AI ChairVerified email at hec.ca
Connor W. ColeyMassachusetts Institute of TechnologyVerified email at mit.edu
XuanLong NguyenUniversity of MichiganVerified email at umich.edu
Rafael Gómez-BombarelliAssociate Professor, MIT DMSEVerified email at mit.edu
Xuanzhe LiuBoya Distinguished Professor of Computer Science, Peking 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
Jian PengHelixonVerified email at helixon.com

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	6459	2015
Rotate: Knowledge graph embedding by relational rotation in complex space Z Sun, ZH Deng, JY Nie, J Tang ICLR 2019, 2019	2556	2019
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	1029	2020
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	967	2015
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	800	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	674	2021
Scientific discovery in the age of artificial intelligence H Wang, T Fu, Y Du, W Gao, K Huang, Z Liu, P Chandak, S Liu, ... Nature 620 (7972), 47-60, 2023	625	2023
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	578	2018
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	517	2016
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	507	2020
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	491	2019
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	460	2020
GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation M Xu, L Yu, Y Song, C Shi, S Ermon, J Tang ICLR 2022 Oral, 2022	457	2022
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	377	2014
GMNN: Graph Markov Neural Networks M Qu, Y Bengio, J Tang ICML 2019, 2019	332	2019
Neural bellman-ford networks: A general graph neural network framework for link prediction Z Zhu, Z Zhang, LP Xhonneux, J Tang Advances in Neural Information Processing Systems 34, 29476-29490, 2021	303	2021
Pre-training Molecular Graph Representation with 3D Geometry S Liu, H Wang, W Liu, J Lasenby, H Guo, J Tang ICLR 2022, 2022	301	2022
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	300	2021
Adversarial network embedding Q Dai, Q Li, J Tang, D Wang Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018	258	2018
Learning gradient fields for molecular conformation generation C Shi, S Luo, M Xu, J Tang ICML’21, 2021	209	2021

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