论文标题
揭示图形对比度学习中的结构公平性
Uncovering the Structural Fairness in Graph Contrastive Learning
论文作者
论文摘要
最近的研究表明，对于低度节点，图表卷积网络（GCN）的性能通常更糟，表现出对现实世界中长尾分布的图表的所谓结构不公平性。与GCN和对比度学习的力量结合的图形对比度学习（GCL）已成为一种有希望的学习节点表示的有前途的自我监督方法。 GCL在结构公平方面的表现如何？令人惊讶的是，我们发现通过GCL方法获得的表示形式已经比GCN学到的更偏见更公平。从理论上讲，我们表明这种公平源于GCL的社区内集中度和社区间散射特性，从而导致了非常清晰的社区结构，使其远离社区边界。基于我们的理论分析，我们进一步设计了一种新型的图表增强方法，称为学位偏差（等级）的图形对比度学习，该方法将不同的策略应用于低度和高度节点。对各种基准和评估方案的广泛实验验证了所提出的方法的有效性。
Recent studies show that graph convolutional network (GCN) often performs worse for low-degree nodes, exhibiting the so-called structural unfairness for graphs with long-tailed degree distributions prevalent in the real world. Graph contrastive learning (GCL), which marries the power of GCN and contrastive learning, has emerged as a promising self-supervised approach for learning node representations. How does GCL behave in terms of structural fairness? Surprisingly, we find that representations obtained by GCL methods are already fairer to degree bias than those learned by GCN. We theoretically show that this fairness stems from intra-community concentration and inter-community scatter properties of GCL, resulting in a much clear community structure to drive low-degree nodes away from the community boundary. Based on our theoretical analysis, we further devise a novel graph augmentation method, called GRAph contrastive learning for DEgree bias (GRADE), which applies different strategies to low- and high-degree nodes. Extensive experiments on various benchmarks and evaluation protocols validate the effectiveness of the proposed method.
