Learning fair graph representation through graph information disentanglement.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Graph Representation Learning

Background:

• Graph Neural Networks (GNNs) are powerful for graph-structured data.
• GNNs can perpetuate biases from node attributes and graph topology, leading to unfair predictions.
• Current debiasing methods struggle due to entangled representations.

Purpose of the Study:

• To propose FairGID, a novel framework for fair graph representation learning.
• To enhance fairness by separating topology and node attributes.
• To disentangle node representations for improved debiasing effectiveness.

Main Methods:

• FairGID learns attribute-only and structure-only representations independently.
• Attribute representations are disentangled into latent factors with sensitive attribute masking.
• An adversarial fusion module integrates representations for a fair and informative embedding.

Main Results:

• FairGID demonstrates a superior accuracy-fairness trade-off.
• Experiments on five real-world datasets validate the framework's effectiveness.
• The proposed method outperforms state-of-the-art baselines in fairness.

Conclusions:

• FairGID offers an effective solution for mitigating bias in graph representation learning.
• Separating and disentangling representations is key to achieving fairness.
• The framework shows promise for developing equitable AI systems on graph data.