12 citations · 15 across the 6 of their papers we have counts for
6 papers
Leveraging Invariant Principle for Heterophilic Graph Structure Distribution Shifts
Jinluan Yang, Zhengyu Chen, Teng Xiao +3
Heterophilic Graph Neural Networks (HGNNs) have shown promising results for semi-supervised learning tasks on graphs. Notably, most real-world heterophilic graphs are composed of a…
Discovering Invariant Neighborhood Patterns for Heterophilic Graphs
Jinluan Yang, Ruihao Zhang, Zhengyu Chen +4
This paper studies the problem of distribution shifts on non-homophilous graphs Mosting existing graph neural network methods rely on the homophilous assumption that nodes from the…
Learning to Reweight for Graph Neural Network
Zhengyu Chen, Teng Xiao, Kun Kuang +6
Graph Neural Networks (GNNs) show promising results for graph tasks. However, existing GNNs' generalization ability will degrade when there exist distribution shifts between testin…
Certifiably Robust Graph Contrastive Learning
Minhua Lin, Teng Xiao, Enyan Dai +2
Graph Contrastive Learning (GCL) has emerged as a popular unsupervised graph representation learning method. However, it has been shown that GCL is vulnerable to adversarial attack…
Simple and Asymmetric Graph Contrastive Learning without Augmentations
Teng Xiao, Huaisheng Zhu, Zhengyu Chen +1
Graph Contrastive Learning (GCL) has shown superior performance in representation learning in graph-structured data. Despite their success, most existing GCL methods rely on prefab…
Reconsidering Learning Objectives in Unbiased Recommendation with Unobserved Confounders
Teng Xiao, Zhengyu Chen, Suhang Wang
This work studies the problem of learning unbiased algorithms from biased feedback for recommendation. We address this problem from a novel distribution shift perspective. Recent w…