Tinglin Huang

I am a second-year Ph.D. student at Yale University, advised by Prof. Rex Ying.

Previously, I worked with Prof. Jie Tang from Tsinghua University, and Prof. Tat-Seng Chua from National University of Singapore.

My research interests revolve around graph neural networks and computational biology.

Publications (* denotes equal contribution)

Learning to Group Auxiliary Datasets for Molecule
Tinglin Huang, Ziniu Hu, Rex Ying
NeurIPS, 2023
PDF

A routing-based molecule dataset grouping method.

BatchSampler: Sampling Mini-Batches for Contrastive Learning in Vision, Language, and Graphs
Zhen Yang*, Tinglin Huang*, Ming Ding*, Yuxiao Dong, Rex Ying, Yukuo Cen, Yangliao Geng, Jie Tang
KDD, 2023
PDF / Code

A global negative sampling method for contrastive learning.

GRAND+: Scalable Graph Random Neural Networks
Wenzheng Feng, Yuxiao Dong, Tinglin Huang, Ziqi Yin, Xu Cheng, Evgeny Kharlamov, Jie Tang
WWW, 2022
PDF / Code

A scalable GNN framework for semi-supervised graph learning.

MixGCF: An Improved Training Method for Graph Neural Network-based Recommender Systems
Tinglin Huang, Yuxiao Dong, Ming Ding, Zhen Yang, Wenzheng Feng, Xinyu Wang, Jie Tang
KDD, 2021
PDF / Code

A general negative sampling plugin for GNN-based recommender systems.

Learning Intents behind Interactions with Knowledge Graph for Recommendation
Xiang Wang*, Tinglin Huang*, Dingxian Wang, Yancheng Yuan, Zhenguang Liu, Xiangnan He, Tat-Seng Chua
WWW, 2021
PDF / Code

We proposed KGIN which (1) uncovers useritem relationships at the granularity of intents, and (2) applies relational path-aware aggregation.

Last updated June 2023.
Template from Jon Barron.