2 papers
cs.LG2023
Understanding Community Bias Amplification in Graph Representation Learning
Shengzhong Zhang, Wenjie Yang, Yimin Zhang +3
In this work, we discover a phenomenon of community bias amplification in graph representation learning, which refers to the exacerbation of performance bias between different clas…
cs.LG2023
Rethinking Semi-Supervised Imbalanced Node Classification from Bias-Variance Decomposition
Liang Yan, Gengchen Wei, Chen Yang +2
This paper introduces a new approach to address the issue of class imbalance in graph neural networks (GNNs) for learning on graph-structured data. Our approach integrates imbalanc…