Google Scholar DBLP

Tutorials

• Jian Tang, Fei Wang, Feixiong Cheng. “Artificial Intelligence for Drug Discovery”. KDD’2021.
• Jian Tang, Fei Wang, Feixiong Cheng. “Artificial Intelligence for Drug Discovery”. AAAI’2021.
• William L. Hamilton and Jian Tang. “Graph Representation Learning”. Tutorial at the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI’19), Hawaii, USA, 2019.
• Jian Tang, Cheng Li and Qiaozhu Mei. “Learning representations of networks”. Tutorial at KDD’17.

Preprints

• Andreea Deac, Pierre-Luc Bacon, Jian Tang. “Graph neural induction of value iteration.”, arXiv:2009.12604.
• Simeon Spasov, Alessandro Di Stefano, Pietro Liò, Jian Tang. “GRADE: Graph Dynamic Embedding.”, arXiv:2007.08060.
• Hannah Alsdurf, Yoshua Bengio, Tristan Deleu, Prateek Gupta, Daphne Ippolito, Richard Janda, Max Jarvie, Tyler Kolody, Sekoul Krastev, Tegan Maharaj, Robert Obryk, Dan Pilat, Valerie Pisano, Benjamin Prud’homme, Meng Qu, Nasim Rahaman, Irina Rish, Jean-Franois Rousseau, Abhinav Sharma, Brooke Struck, Jian Tang, Martin Weiss, Yun William Yu. “COVI White Paper.”, arXiv:2005.08502.
• Jordan Hoffmann, Louis Maestrati, Yoshihide Sawada, Jian Tang, Jean Michel Sellier, Yoshua Bengio. “Data-Driven Approach to Encoding and Decoding 3-D Crystal Structures”, arXiv:1909.00949.

2023

• Chence Shi, Chuanrui Wang, Jiarui Lu, Bozitao Zhong, Jian Tang. Protein Sequence and Structure Co-Design with Equivariant Translation. ICLR 2023.
• Jianan Zhao, Meng Qu, Chaozhuo Li, Hao Yan, Qian Liu, Rui Li, Xing Xie, Jian Tang. Learning on Large-scale Text-attributed Graphs via Variational Inference. ICLR 2023.
• Yangtian Zhang, Huiyu Cai, Chence Shi, Jian Tang. E3Bind: An End-to-End Equivariant Network for Protein-Ligand Docking. ICLR 2023.
• Zuobai Zhang, Minghao Xu, Arian Rokkum Jamasb, Vijil Chenthamarakshan, Aurelie Lozano, Payel Das, Jian Tang. Protein Representation Learning by Geometric Structure Pretraining. ICLR 2023.
• Shengchao Liu, Hongyu Guo, Jian Tang. Molecular Geometry Pretraining with SE(3)-Invariant Denoising Distance Matching. ICLR 2023.
• Yu Li, Meng Qu, Jian Tang, Yi Chang. “Signed Laplacian Graph Neural Networks”. AAAI 2023.

2022

• Minghao Xu, Zuobai Zhang, Jiarui Lu, Zhaocheng Zhu, Yangtian Zhang, Chang Ma, Runcheng Liu, Jian Tang. PEER: A Comprehensive and Multi-Task Benchmark for Protein Sequence Understanding. NeurIPS Datasets and Benchmarks 2022.
• Mikhail Galkin, Zhaocheng Zhu, Hongyu Ren, Jian Tang. Inductive Logical Query Answering in Knowledge Graphs. NeurIPS 2022.
• Chenqing Hua, Guillaume Rabusseau, Jian Tang. High-Order Pooling for Graph Neural Networks with Tensor Decomposition. NeurIPS 2022.
• Shaohua Fan, Xiao Wang, Yanhu Mo, Chuan Shi, Jian Tang. Debiasing Graph Neural Networks via Learning Disentangled Causal Substructure. NeurIPS 2022.
• Kuangqi Zhou, Kaixin Wang, Jian Tang, Jiashi Feng, Bryan Hooi, Peilin Zhao, Tingyang Xu, Xinchao Wang. Jointly Modelling Uncertainty and Diversity for Active Molecular Property Prediction. Learning on Graphs Conference 2022.
• Wujie Wang, Minkai Xu, Chen Cai, Benjamin Kurt Miller, Tess Smidt, Yusu Wang, Jian Tang, Rafael Gomez-Bombarelli. Generative Coarse-Graining of Molecular Conformations. ICML’22.
• Zhaocheng Zhu, Mikhail Galkin, Zuobai Zhang, Jian Tang. Neural-Symbolic Models for Logical Queries on Knowledge Graphs, ICML’22.
• Sean Bin Yang, Chenjuan Guo, Jilin Hu, Bin Yang, Jian Tang, and Christian S. Jensen. “Weakly-supervised Temporal Path Representation Learning with Contrastive Curriculum Learning”, ICDE’22.
• Jing Zhang, Xiaokang Zhang, Jifan Yu, Jian Tang, Jie Tang, Cuiping Li, Hong Chen. “Subgraph Retrieval Enhanced Model for Multi-hop Knowledge Base Question Answering”, ACL’22.
• Meng Qu, Huiyu Cai, Jian Tang. [Neural Structured Prediction for InductiveNode Classification], ICLR’22.
• Minkai Xu, Lantao Yu, Yang Song, Chence Shi, Stefano Ermon, Jian Tang. [GeoDiff: A Geometric Diffusion Model for Molecular Conformation Generation], ICLR’22.
• Shengchao Liu, Hanchen Wang, Weiyang Liu, Joan Lasenby, Hongyu Guo, Jian Tang. Pre-training Molecular Graph Representation with 3D Geometry, ICLR’22.
• Shengchao Liu, Meng Qu, Zuobai Zhang, Huiyu Cai, Jian Tang. [Structured Multi-Task Learning for Molecular Property Predictions], AISTATS’22.

2021

• Zhaocheng Zhu, Zuobai Zhang, Louis-Pascal A. C. Xhonneux, Jian Tang. Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction, to appear at NeurIPS’21.
• Shitong Luo, Chence Shi, Minkai Xu, Jian Tang. Predicting Molecular Conformation via Dynamic Graph Score Matching, to appear at NeurIPS’21.
• Minghao Xu, Meng Qu, Bingbing Ni, Jian Tang. Joint Modeling of Visual Objects and Relations for Scene Graph Generation, to appear at NeurIPS’21.
• Andreea Deac, Petar Veličković, Ognjen Milinković, Pierre-Luc Bacon, Jian Tang, Mladen Nikolic. Neural Algorithmic Reasoners are Implicit Planners, to appear at NeurIPS’21.
• Louis-Pascal A. C. Xhonneux, Andreea Deac, Petar Veličković, Jian Tang.How to transfer algorithmic reasoning knowledge to learn new algorithms?, to appear at NeurIPS’21.
• Yifan Zhao†, Huiyu Cai†, Zuobai Zhang, Jian Tang*, Yue Li* (2021). “Learning interpretable cellular and gene signature embeddings from single-cell transcriptomic data”. Nature Communications (accepted). Preprint: bioRxiv 2021.01.13.426593 (†equal contribution; *co-corresponding authors)
• Chence Shi, Shitong Luo, Minkai Xu, Jian Tang. “Learning Gradient Fields for Molecular Conformation Generation”, to appear at ICML’21.
• Minkai Xu, Wujie Wang, Shitong Luo, Chence Shi, Yoshua Bengio, Rafael Gomez-Bombarelli, Jian Tang. “An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming”, to appear at ICML’21.
• Hangrui Bi, Hengyi Wang, Chence Shi, Connor Coley, Jian Tang, Hongyu Guo. “Non-Autoregressive Electron Redistribution Modeling for Reaction Prediction”, to appear at ICML’21.
• Minghao Xu, Hang Wang, Bingbing Ni, Hongyu Guo, Jian Tang. “Self-supervised Graph-level Representation Learning with Local and Global Structure ”, to appear at ICML’21.
• Sean Bin Yang, Chenjuan Guo,Jilin Hu,Jian Tang, Bin Yang. “Unsupervised Path Representation Learning with Curriculum Negative Sampling”, IJCAI’21.
• Meng Qu, Junkun Chen, Louis-Pascal AC Xhonneux, Yoshua Bengio, Jian Tang. “RNNLogic: Learning Logic Rules for Reasoning on Knowledge Graphs”, ICLR’2021
• Minkai Xu, Shitong Luo, Yoshua Bengio, Jian Peng, Jian Tang. “Learning Neural Generative Dynamics for Molecular Conformation Generation”, ICLR’2021
• Yoshua Bengio, Prateek Gupta, Tegan Maharaj, Nasim Rahaman, Martin Weiss, Tristan Deleu, Eilif Benjamin Muller, Meng Qu, victor schmidt, Pierre-luc St-charles, hannah alsdurf, Olexa Bilaniuk, david buckeridge, gaetan caron, pierre luc carrier, Joumana Ghosn, satya ortiz gagne, Christopher Pal, Irina Rish, Bernhard Schölkopf, abhinav sharma, Jian Tang, andrew williams. “Predicting Infectiousness for Proactive Contact Tracing”, ICLR’2021
• Vikas Verma, Meng Qu, Alex Lamb, Yoshua Bengio, Juho Kannala, Jian Tang. “GraphMix: Regularized Training of Graph Neural Networks for Semi-Supervised Learning”, AAAI’2021.

2020

• Xiaozhi Wang, Tianyu Gao, Zhaocheng Zhu, Zhiyuan Liu, Juanzi Li, Jian Tang. “KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation”, TACL 2020.
• Yadi Zhou, Fei Wang, Jian Tang, Ruth Nussinov, Feixiong Cheng. “Artificial intelligence in COVID-19 drug repurposing.”, The Lancet Digital Health 2020.
• Shengding Hu, Zheng Xiong, Meng Qu, Xingdi Yuan, Marc-Alexandre Côté, Zhiyuan Liu, Jian Tang. “Graph Policy Network for Transferable Active Learning on Graphs”, NeurIPS’2020
• Ashutosh Adhikari, Xingdi Yuan, Marc-Alexandre Côté, Mikuláš Zelinka, Marc-Antoine Rondeau, Romain Laroche, Pascal Poupart, Jian Tang, Adam Trischler, William L Hamilton. “Learning dynamic knowledge graphs to generalize on text-based games”, NeurIPS’2020
• Wangchunshu Zhou, Jinyi Hu, Hanlin Zhang, Xiaodan Liang, Maosong Sun, Chenyan Xiong, Jian Tang. “Towards Interpretable Natural Language Understanding with Explanations as Latent Variables”, NeurIPS’2020
• Chence Shi, Minkai Xu, Hongyu Guo, Ming Zhang, Jian Tang. “A Graph to Graphs Framework for Retrosynthesis Prediction”, ICML’20.
• Meng Qu, Tianyu Gao, Louis-Pascal AC Xhonneux, Jian Tang. “Few-shot Relation Extraction via Bayesian Meta-learning on Task Graphs”, ICML’20.
• Louis-Pascal AC Xhonneux, Meng Qu, Jian Tang. “Continuous Graph Neural Networks”, ICML’20.
• Sai Krishna Gottipati, Boris Sattarov, Sufeng Niu, Haoran Wei, Yashaswi Pathak, Shengchao Liu, Simon Blackburn, Karam Thomas, Connor Coley, Jian Tang, Sarath Chandar, Yoshua Bengio. “Learning to Navigate in Synthetically Accessible Chemical Space Using Reinforcement Learning”, ICML’20.
• Chence Shi, Minkai Xu, Zhaocheng Zhu, Weinan Zhang, Ming Zhang, Jian Tang. “GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation”. To appear at the International Conference on Learning Representations 2020 (ICLR’20), Addis Ababa, Ethiopia, Apr.26-Apr. 30, 2020
• Fan-Yun Sun, Jordan Hoffmann, Jian Tang.”InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization.” To appear at the International Conference on Learning Representations 2020 (ICLR’20, Spotlight), Addis Ababa, Ethiopia, Apr.26-Apr.30, 2020

2019

• Meng Qu, Jian Tang. “Probabilistic Logic Neural Networks for Reasoning”, to appear at the Thirty-third Annual Conference on Neural Information Processing Systems (NeurIPS‘19), Vancouver, Canada, Dec.8-15, 2019. source
• Fan-Yun Sun, Meng Qu, Jordan Hoffmann, Chin-Wei Huang, Jian Tang. “vGraph: A Generative Model for Joint Community Detection and Node Representation Learning”, to appear at the Thirty-third Annual Conference on Neural Information Processing Systems (NeurIPS’19), Vancouver, Canada, Dec.8-15, 2019.
• Weiping Song, Chence Shi, Zhiping Xiao, Zhijian Duan, Yewen Xu, Ming Zhang, Jian Tang. “AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks.” In the 28th ACM International Conference on Information and Knowledge Management (CIKM’19), November 3rd-7th, Beijing, China. source
• Meng Qu, Yoshua Bengio, and Jian Tang. “GMNN: Graph Markov Neural Networks”. To appear at the 36th International Conference on Machine Learning (ICML’19), Long Beach, California, United States. source
• Yanru Qu, Ting Bai, Weinan Zhang, Jianyun Nie, Jian Tang. “An End-to-End Neighborhood-based Interaction Model for Knowledge-enhanced Recommendation”. In the first Workshop on Deep Learning Practice for High-dimensional Space data, KDD 2019, Alaska, US. (Best paper)
• Andreea Deac, Yu-Hsiang Huang, Petar Veličković, Pietro Liò, Jian Tang. “Drug-Drug Adverse Effect Prediction with Graph Co-Attention”, arXiv:1905.00534, Workshop on Computational Biology at the 36th International Conference on Machine Learning (ICML 2019).
• Cheng Yang, Jian Tang, Maosong Sun, Ganqu Cui, and Zhiyuan Liu. “Multi-scale Information Diffusion Prediction with Reinforced Recurrent Networks”. To appear in the International Joint Conference on Artificial Intelligence (IJCAI’19), August 10-16 2019, Macao, China.
• Zhiqing Sun, Jian Tang, Pan Du, Zhi-Hong Deng and Jian-Yun Nie. “DivGraphPointer: A Graph Pointer Network for Extracting Diverse Keyphrases.” In the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’19), July 21-25, 2019, Paris, France.
• Zhaocheng Zhu, Shizhen Xu, Meng Qu, Jian Tang. “GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding”. In the Web Conference 2019 (formerly known as WWW’2019), San Francisco, CA, USA, May 13-17, 2019. website
• Zhiqing Sun, Zhi-Hong Deng, Jian-Yun Nie, Jian Tang. “RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space.” In the Seventh International Conference on Learning Representations (ICLR’19), New Orleans, USA. source
• William L. Hamilton and Jian Tang. “Graph Representation Learning”. Tutorial at the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI’19), Hawaii, USA, 2019. (Part 0, Part I, Part II, Part III)
• Pengfei Liu, Shuaichen Chang, Xuanjing Huang, Jian Tang, Jackie Chi Kit Cheung. “Contextualized Non-local Neural Networks for Sequence Learning.” In the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI’19), Hawaii, USA, 2019. Mingjie Sun, Jian Tang, Huichen Li, Bo Li, Chaowei Xiao, Yao Chen, Dawn Song. “Data Poisoning Attack against Unsupervised Node Embedding Methods.” arXiv:1810.12881
• Weiping Song, Zhiping Xiao, Yifan Wang, Laurent Charlin, Ming Zhang and Jian Tang. “Session-based Social Recommendation via Dynamic Graph Attention Networks.” To appear at the 12th ACM International Conference on Web Search and Data Mining (WSDM’19), Melbourne, Australia, February 11-15, 2019. source

2018 and before

• Jiezhong Qiu, Jian Tang, Hao Ma, Yuxiao Dong, Kuansan Wang, and Jie Tang. DeepInf: Modeling Influence Locality in Large Social Networks. In Proceedings of the Twenty-Fourth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining KDD’18
• Minjeong Kim, Minsuk Choi, Sunwoong Lee, Jian Tang, Haesun Park, and Jaegul Choo. PixelSNE: Pixel-Aligned Stochastic Neighbor Embedding for Efficient 2D Visualization with Screen-Resolution Precision. In 20th EG / VGTC Conference on Visualization (EuroVis’18)
• Quanyu Dai, Qiang Li, Jian Tang, and Dan Wang. Adversarial Network Embedding, In Proc. of 2018 AAAI Conf. on Artificial Intelligence (AAAI’18), New Orleans, LA, Feb. 2018
• Luchen Liu, Jianhao Shen, Ming Zhang, Zichang Wang, and Jian Tang. Learning the Joint Representation of Heterogeneous Temporal Events for Clinical Event Prediction, in Proc. of 2018 AAAI Conf. on Artificial Intelligence (AAAI’18), New Orleans, LA, Feb. 2018
• Meng Qu, Jian Tang, and Jiawei Han, Curriculum Learning for Heterogeneous Star Network Embedding via Deep Reinforcement Learning, in Proc. of 2018 ACM Int. Conf. on Web Search and Data Mining (WSDM’18), Los Angeles, CA, Feb. 2018 source code
• Meng Qu, Jian Tang, Jingbo Shang, Xiang Ren, Ming Zhang, Jiawei Han. An Attention-based Collaboration Framework for Multi-View Network Representation Learning, in Proc. of 2017 ACM Int. Conf. on Information and Knowledge Management (CIKM’17), Singapore, Nov. 2017 source code
• Jian Tang, Yue Wang, Kai Zheng and Qiaozhu Mei. End-to-end learning for short text expansion. KDD’17
• Xuanzhe Liu*, Wei Ai*, Huoran Li, Jian Tang, Gang Huang, and Qiaozhu Mei, Derive User Preferences of Mobile Apps from their Management Activities, in ACM Transactions on Information Systems (TOIS) , in press, 2017
• Jian Tang, Jingzhou Liu, Ming Zhang and Qiaozhu Mei. Visualizing Large-scale and High-dimensional Data. WWW’16. (Best paper nomination 5/727)
• Huoran Li, Wei Ai, Xuanzhe Liu, Jian Tang, Gang Huang, Feng Feng, and Qiaozhu Mei. Voting with Their Feet: Inferring User Preferences from App Management Activities. WWW’16 (industry track).
• Jian Tang, Meng Qu, and Qiaozhu Mei. PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks. KDD’15.
• Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan and Qiaozhu Mei. LINE: Large-scale Information Network Embedding. WWW’15. source code (Most cited paper in WWW’15)
• Jian Tang, Ming Zhang, and Qiaozhu Mei. “Look Ma, No Hands!” A parameter-free topic model. 2014
• Yong Luo, Jian Tang, Jun Yan, Chao Xu, and Zheng Chen. Pre-trained multi-view word embedding using two-side neural network. AAAI’14.
• Jian Tang, Zhaoshi Meng, XuanLong Nguyen, Qiaozhu Mei and Ming Zhang. Understanding the limiting factors of topic modeling via posterior contraction analysis. In proceedings of the 31st International Conference on Machine Learning (ICML), Beijing, June 2014. (Best paper award, 1/1500)
• Jian Tang, Ming Zhang, and Qiaozhu Mei. One theme in all views: Modeling consensus topics in multiple contexts. In Proceedings of the 2013 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’13). (17% acceptance)
• Jian Tang, Jun Yan, Lei Ji, Ming Zhang, Shaodan Guo, Ning Liu, Xianfang Wang, and Zheng Chen. Collaborative users’ brand preference across multiple domains from implicit feedbacks. AAAI 2011. PP447-482.
• Jian Tang, Ning Liu, Jun Yan, Yelong Shen, Shaodan Guo, Bin Gao, Shuicheng Yan, and Ming Zhang. Learning to rank audience for behavioral targeting in display ads. CIKM 2011: 605-610
• Lei Zhang, Jian Tang, and Ming Zhang. Integrating temporal usage pattern into personalized tag prediction. APWeb 2012: 354-365.
• Ming Zhang, Sheng Feng, Jian Tang, Bolanle Ojokoh, and Guojun Liu. Co-ranking multiple entities in a heterogeneous network: Integrating temporal factor and users’ bookmarks. ICADL 2011, Beijing, China. Springer LNCS 7008, PP.202-211.
• Bolanle Ojokoh, Ming Zhang, and Jian Tang. A trigram hidden Markov model for metadata extraction from heterogeneous references. Information Science , Volume 181, Issue 9, 1 May 2011, Pages 1538-1551 (Elsevier Press).
• Fei Yan, Ming Zhang, Jian Tang, Tao Sun, Zhi-Hong Deng, and Long Xiao. Users’ book-loan behaviors analysis and knowledge dependency mining. WAIM 2010: 206-217.
• Yan Fei, Zhang Ming, Tan Yuwei, Tang Jian, and Deng Zhihong. Community discovery based on actors’ interest and social network structure. In the twenty-seventh National Database Conference of China, 2010. (In Chinese).