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Deep Graph Learning: Algorithms and Applications

It is alway challenging to develop effective machine learning algorithms to efficiently process graph data due to highly complex but inforative graph structure. The goal of this project is to attach this challenge by developing novel deep learning models to effectively and efficiently process graph data.


Figure 1. graph data examples

Publication

  1. Ruoyu Li, Sheng Wang, Feiyun Zhu and Junzhou Huang, "Adaptive Graph Convolutional Neural Networks", In Proc. of The Thirty-Second AAAI Conference on Artificial Intelligence, AAAI'18, New Orleans, USA, February 2018. (Oral Presentation) [CODE]
  2. Ruoyu Li, Jiawen Yao, Xinliang Zhu, Yeqing Li and Junzhou Huang, "Graph CNN for Survival Analysis on Whole Slide Pathological Images", In Proc. of the 20th Annual International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI'18, Granada, Spain, September 2018. [CODE]
  3. Wenbing Huang, Tong Zhang, Yu Rong and Junzhou Huang, "Adaptive Sampling Towards Fast Graph Representation Learning", In Proc. of the 32nd Annual Conference on Neural Information Processing Systems, NIPS'18, Montreal, Canada, December 2018.
  4. Jia Li, Yu Rong, Hong Cheng, Helen Meng, Wenbing Huang and Junzhou Huang, "Semi-Supervised Graph Classification: A Hierarchical Graph Perspective", In Proc. of International World Wide Web Conference, WWW'19, San Francisco, CA, USA, May 2019.
  5. Sheng Wang, Zheng Xu, Chaochao Yan and Junzhou Huang, “Graph Convolutional Nets for Tool Presence Detection in Surgical Videos", In Proc. of The 26th International Conference on Information Processing in Medical Imaging, IPMI'19, Hong Kong, China, June 2019.
  6. Runhao Zeng, Wenbing Huang, Chuang Gan, Mingkui Tan, Yu Rong, Peilin Zhao and Junzhou Huang, "Graph Convolutional Networks for Temporal Action Localization", In Proc. of the 17th International Conference on Computer Vision, ICCV'19, Seoul, Korea, October 2019.
  7. Heng Chang, Yu Rong, Tingyang Xu, Wenbing Huang, Honglei Zhang, Peng Cui, Wenwu Zhu and Junzhou Huang, "A Restricted Black-box Adversarial Framework Towards Attacking Graph Embedding Models", In Proc. of the Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI'20, NYC, New York, USA, February 2020.
  8. Huaxiu Yao, Chuxu Zhang, Ying Wei, Meng Jiang, Suhang Wang, Junzhou Huang, Nitesh Chawla, Zhenhui Li, "Graph Few-shot Learning via Knowledge Transfer", In Proc. of the Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI'20, NYC, New York, USA, February 2020.
  9. Tian Bian, Xi Xiao, Tingyang Xu, Peilin Zhao, Wenbing Huang, Yu Rong and Junzhou Huang, "Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks", In Proc. of the Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI'20, NYC, New York, USA, February 2020.
  10. Yu Rong, Wenbing Huang, Tingyang Xu, Junzhou Huang, "DropEdge: Towards Deep Graph Convolutional Networks on Node Classification", In Proc. of International Conference on Learning Representations, ICLR'20, Addis Ababa, Ethiopia, April 2020.
  11. Jia Li, Honglei Zhang, Zhichao Han, Yu Rong, Hong Cheng and Junzhou Huang, "Adversarial Attack on Community Detection by Hiding Individuals", In Proc. of International World Wide Web Conference, WWW'20, Taibei, Tanwan, China, April 2020.
  12. Zhen Peng, Wenbing Huang, Minnan Luo, Qinghua Zheng, Yu Rong, Tingyang Xu and Junzhou Huang, "Graph Representation Learning via Graphical Mutual Information Maximization", In Proc. of International World Wide Web Conference, WWW'20, Taibei, Tanwan, China, April 2020.
  13. Ashwin Raju, Jiawen Yao, Mohammad Minhazul Haq, Jitendra Jonnagaddala and Junzhou Huang, "Graph Attention Multi-instance Learning for Accurate Colorectal Cancer Staging", In Proc. of the 22nd Annual International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI'20, Lima, Peru, October 2020.
  14. Jia Li, Jianwei Yu, Jiajin Li, Honglei Zhang, Kangfei Zhao, Yu Rong, Hong Cheng and Junzhou Huang, "Dirichlet Graph Variational Autoencoder", In Proc. of the 34th Annual Conference on Neural Information Processing Systems, NIPS’20, Vancouver, Canada, December 2020.
  15. Yu Rong, Yatao Bian, Tingyang Xu, Weiyang Xie, Ying Wei, Wenbing Huang and Junzhou Huang, "GROVER: Self-Supervised Message Passing Transformer on Largescale Molecular Graphs", In Proc. of the 34th Annual Conference on Neural Information Processing Systems, NIPS’20, Vancouver, Canada, December 2020.