Debiasing Graph Representation Learning Based on Information Bottleneck.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Data Science

Background:

• Graph representation learning excels in real-world applications but often lacks fairness.
• Existing fair representation learning methods, particularly adversarial ones, can be unstable.
• Addressing discriminatory predictions in graph-based systems is crucial.

Purpose of the Study:

• To develop a stable and effective framework for fair graph representation learning.
• To mitigate bias in graph representation learning without compromising utility.
• To introduce a novel approach that avoids the instability of adversarial methods.

Main Methods:

• Proposed GRAFair, a framework based on a variational graph autoencoder (VGAE).
• Introduced a conditional fairness bottleneck (CFB) to balance representation utility and sensitive information.
• Utilized variational approximation for tractable optimization.

Main Results:

• GRAFair effectively produces informative representations with minimal sensitive information.
• The method demonstrates superior fairness, utility, robustness, and stability compared to existing approaches.
• Experiments on real-world datasets validate the framework's performance.

Conclusions:

• GRAFair offers a stable and effective solution for fair graph representation learning.
• The conditional fairness bottleneck is key to achieving a balance between utility and fairness.
• This work advances the field by providing a non-adversarial, stable method for fair graph AI.