M2D distillation augments input graphs with model-derived features and structure, letting simple student GNNs match teacher performance while exposing mechanisms such as attention and fairness directly in the data.
Towards a unified framework for fair and stable graph representation learning
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
A GNN fairness model edits graphs for higher class homophily and lower sensitive-attribute homophily, then trains with supervised contrastive and environmental losses to improve both accuracy and fairness metrics over prior CAF baselines.
citing papers explorer
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From Model to Data (M2D): Shifting Complexity from GNNs to Graphs for Transparent Graph Learning
M2D distillation augments input graphs with model-derived features and structure, letting simple student GNNs match teacher performance while exposing mechanisms such as attention and fairness directly in the data.
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Homophily-aware Supervised Contrastive Counterfactual Augmented Fair Graph Neural Network
A GNN fairness model edits graphs for higher class homophily and lower sensitive-attribute homophily, then trains with supervised contrastive and environmental losses to improve both accuracy and fairness metrics over prior CAF baselines.