Structure Fairness

Structure fairness in graph neural networks addresses the inherent bias where nodes with low degrees (e.g., peripheral individuals in social networks) are unfairly disadvantaged compared to high-degree nodes in downstream tasks. Current research focuses on developing algorithms and model architectures, such as FairDGE and SFairGNN, that mitigate this bias through techniques like debiasing strategies and attentive information aggregation, aiming to improve the performance of low-degree nodes without sacrificing overall model accuracy. This work is significant because it tackles a fundamental limitation of graph-based machine learning, leading to more equitable and reliable results across various applications, including social network analysis and recommendation systems.