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Learning high-quality node embeddings is a key building block for machinelearning models that operate on graph data, such as social networks andrecommender systems. However, existing graph embedding techniques are unable tocope with fairness constraints, e.g., ensuring that the learned representationsdo not correlate with certain attributes, such as age or gender. Here, weintroduce an adversarial framework to enforce fairness constraints on graphembeddings. Our approach is compositional---meaning that it can flexiblyaccommodate different combinations of fairness constraints during inference.For instance, in the context of social recommendations, our framework wouldallow one user to request that their recommendations are invariant to boththeir age and gender, while also allowing another user to request invariance tojust their age. Experiments on standard knowledge graph and recommender systembenchmarks highlight the utility of our proposed framework.

Compositional Fairness Constraints for Graph Embeddings