Hengrui Zhang

I am a second year Ph.D. student in Computer Science at Univeristy of Illinois, Chicago. My advisor is Philip S. Yu.

Before that, I received my bachelor's degree from Shanghai Jiao Tong University (SJTU).

Email: hzhan55 [at] uic [dot] edu

Google Scholar   /   Github   /   LinkedIn     

profile photo
Research Topics

  • I'm interested in a general topic of machine learning and deep learning on graph data, e.g. Graph Neural Networks.
  • Currently I am actively working on designing effective, efficient and scalable self-supervised methods for unsupervised representation learning on graph-structured data.

Publications (* equal contribution)

Align Representations with Base: A New Approach to Self-Supervised Learning
Shaofeng Zhang, Lyn Qiu, Feng Zhu, Junchi Yan, Hengrui Zhang, Rui Zhao, Hongyang Li, Xiaokang Yang
Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 
Self-supervised Representation Learning, Basis Decomposition.
code

Towards Distribution Shift of Node-level Prediction on Graphs: An Invariance Perspective
Qitian Wu,  Hengrui Zhang,  Junchi Yan,  David Wipf, 
International Conference on Learning Representations (ICLR), 2022. 
Out-Of-Distribution, Graph Neural Networks.
code

From Canonical Correlation Analysis to Self-supervised Graph Neural Networks
Hengrui Zhang,  Qitian Wu,  Junchi Yan,  David Wipf,  Philip S. Yu, 
Advances in Neural Information Processing Systems (NeurIPS), 2021.
Self-supervised Learning, Canonical Correlation Analysis.
code

Towards Open-world Recommendation: An Inductive Model-based Collaborative Filtering Approach
Qitian Wu,  Hengrui Zhang,  Xiaofeng Gao,  Junchi Yan,  Hongyuan Zha, 
International Conference on Machine Learning (ICML), 2021. 
Inductive Collaborative Filtering, Recommender System.
code

Stacked Mixed-Order Graph Convolutional Networks for Collaborative Filtering
Hengrui Zhang,  Julian McAuley, 
SIAM Data Mining (SDM), 2020. 
Collaborative Filtering, Graph Neural Network, High-order Connectivity.

Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social E€ects in Recommender Systems
Qitian Wu, Hengrui Zhang, Xiaofeng Gao, Peng He, Paul Weng, Han Gao, Guihai Chen
The Web Conference (WWW), 2019. 
Social Recommendation, Graph Attention Networks.
code

Adversarial Training Model Unifying Feature Driven and Point Process Perspectives for Event Popularity Prediction
Qitian Wu, Chaoqi Yang, Hengrui Zhang, Xiaofeng Gao, Paul Weng, Guihai Chen
The Conference on Information and Knowledge Management (CIKM), 2018. 
Hawkes Process, Adversarial Training.

Professional Services
  • PC member/Reviewer: CVPR'22, ICML'22.



Updated at Mar. 2022
Thanks Jon Barron for this amazing work