10 citations · 39 across the 6 of their papers we have counts for
6 papers
Augmentation-Free Graph Contrastive Learning with Performance Guarantee
Haonan Wang, Jieyu Zhang, Qi Zhu +1
Graph contrastive learning (GCL) is the most representative and prevalent self-supervised learning approach for graph-structured data. Despite its remarkable success, existing GCL…
FairIF: Boosting Fairness in Deep Learning via Influence Functions with Validation Set Sensitive Attributes
Haonan Wang, Ziwei Wu, Jingrui He
Most fair machine learning methods either highly rely on the sensitive information of the training samples or require a large modification on the target models, which hinders their…
From Intrinsic to Counterfactual: On the Explainability of Contextualized Recommender Systems
Yao Zhou, Haonan Wang, Jingrui He +1
With the prevalence of deep learning based embedding approaches, recommender systems have become a proven and indispensable tool in various information filtering applications. Howe…
Controllable Gradient Item Retrieval
Haonan Wang, Chang Zhou, Carl Yang +2
In this paper, we identify and study an important problem of gradient item retrieval. We define the problem as retrieving a sequence of items with a gradual change on a certain att…
Secure Deep Graph Generation with Link Differential Privacy
Carl Yang, Haonan Wang, Ke Zhang +2
Many data mining and analytical tasks rely on the abstraction of networks (graphs) to summarize relational structures among individuals (nodes). Since relational data are often sen…
Relation Learning on Social Networks with Multi-Modal Graph Edge Variational Autoencoders
Carl Yang, Jieyu Zhang, Haonan Wang +5
While node semantics have been extensively explored in social networks, little research attention has been paid to profile edge semantics, i.e., social relations. Ideal edge semant…