Understanding Community Bias Amplification in Graph Representation Learning

• URL: http://arxiv.org/abs/2312.04883v1
• Date: Fri, 8 Dec 2023 07:43:05 GMT
• Title: Understanding Community Bias Amplification in Graph Representation Learning
• Authors: Shengzhong Zhang, Wenjie Yang, Yimin Zhang, Hongwei Zhang, Divin Yan, Zengfeng Huang
• Abstract summary: We study a phenomenon of community bias amplification in graph representation learning. We propose a novel graph contrastive learning model called Random Graph Coarsening Contrastive Learning.
• Score: 22.522798932536038
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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