  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 age or gender. Here, we
introduce an adversarial framework to enforce fairness constraints on graph
embeddings. Our approach is compositional---meaning that it can flexibly
accommodate different combinations of fairness constraints during inference.
For instance, in the context of social recommendations, our framework would
allow one user to request that their recommendations are invariant to both
their age and gender, while also allowing another user to request invariance to
just their age. Experiments on standard knowledge graph and recommender system
benchmarks highlight the utility of our proposed framework.
