Publications

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.

2022

  • Meng Qu, Huiyu Cai, Jian Tang. [Neural Structured Prediction for InductiveNode Classification], to appear at ICLR’22.
  • Minkai Xu, Lantao Yu, Yang Song, Chence Shi, Stefano Ermon, Jian Tang. [GeoDiff: A Geometric Diffusion Model for Molecular Conformation Generation], to appear at ICLR’22.
  • Shengchao Liu, Hanchen Wang, Weiyang Liu, Joan Lasenby, Hongyu Guo, Jian Tang. Pre-training Molecular Graph Representation with 3D Geometry, to appear at ICLR’22.
  • Shengchao Liu, Meng Qu, Zuobai Zhang, Huiyu Cai, Jian Tang. [Structured Multi-Task Learning for Molecular Property Predictions], to appear at AISTATS’22.

2021

2020

2019

2018 and before