Summary of Comfairgnn: Community Fair Graph Neural Network, by Yonas Sium et al.
ComFairGNN: Community Fair Graph Neural Network
by Yonas Sium, Qi Li
First submitted to arxiv on: 7 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A Graph Neural Network (GNN) debiasing framework is proposed to mitigate community-level bias in GNNs, which can produce biased predictions against certain demographic subgroups due to node attributes and neighbors. The current research on GNN fairness focuses on oversimplified evaluation metrics, leading to a misleading impression of fairness. This paper examines the effectiveness of existing debiasing methods in unfairness evaluation and introduces a novel framework called ComFairGNN that employs a learnable coreset-based debiasing function to address bias from local neighborhood distributions during GNN aggregation. The framework is evaluated on three benchmark datasets, demonstrating its effectiveness in both accuracy and fairness metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Graph Neural Networks (GNNs) can be biased against certain groups of people. This happens because the network looks at what’s happening around each node. To fix this, we need better ways to test how fair GNNs are being. Right now, those tests aren’t very good and give us a wrong idea of fairness. We want to improve that. Our new approach is called ComFairGNN. It uses something called a “coreset” to help the network be more fair. We tested it on some big datasets and it worked really well. |
Keywords
» Artificial intelligence » Gnn » Graph neural network
