Compositional Fairness Constraints for Graph Embeddings

Learning high-quality node embeddings is a key building block for machine learning models that operate on graph data, such as social networks and recommender systems. However, existing graph embedding techniques are unable to cope with fairness constraints, e.g., ensuring that the learned representations do not correlate with certain attributes, such as race or gender. Here, we introduce an adversarial framework to enforce fairness constraints on graph embeddings. Our approach is {\em compositional}---meaning that it can (optionally) enforce multiple different fairness constraints during inference. Experiments on standard knowledge graph and recommender system benchmarks highlight the utility of our proposed framework.