Understanding Community Bias Amplification in Graph Representation
  Learning
Shengzhong Zhang Wenjie Yang Yimin Zhang Hongwei Zhang Divin Yan Zengfeng Huang
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 classes by graph representation learning. We conduct an
in-depth theoretical study of this phenomenon from a novel spectral
perspective. Our analysis suggests that structural bias between communities
results in varying local convergence speeds for node embeddings. This
phenomenon leads to bias amplification in the classification results of
downstream tasks. Based on the theoretical insights, we propose random graph
coarsening, which is proved to be effective in dealing with the above issue.
Finally, we propose a novel graph contrastive learning model called Random
Graph Coarsening Contrastive Learning (RGCCL), which utilizes random coarsening
as data augmentation and mitigates community bias by contrasting the coarsened
graph with the original graph. Extensive experiments on various datasets
demonstrate the advantage of our method when dealing with community bias
amplification.