Analyzing the Effect of Sampling in GNNs on Individual Fairness

Sampling methods used in graph neural networks can undermine fairness guarantees: The paper demonstrates that when GNNs like GraphSAGE use mini-batch training with random sampling to handle large datasets, this process can compromise individual fairness protections that work well on complete graphs, highlighting a practical trade-off between scalability and fairness.

Existing fairness methods don't easily scale to real-world recommendation systems: The researchers identify that current fairness promotion techniques are difficult to apply to large, real-world datasets, creating a significant gap between fairness theory and practical implementation in popular recommendation systems that use GNNs.

Graph structure introduces new fairness challenges: The paper shows that representing data as networks (graphs) in recommendation systems creates unique bias problems that are more complex than those in traditional machine learning systems, requiring specialized approaches to address algorithmic bias.

Individual fairness through rank optimization shows promise for graph-based systems: The REDRESS method, which learns fair embeddings through rank optimization, provides a potential pathway for promoting fairness in GNN-based recommendations, though its effectiveness depends on how the data is sampled during training.

Different GNN architectures have different fairness implications: The comparison between GCN (which uses full graph data) and GraphSAGE (which uses sampled subgraphs) reveals that architectural choices in neural networks have measurable effects on fairness outcomes, suggesting practitioners must consider fairness when selecting GNN methods.