Enforcing fairness in graph representation learning

As the representations output by Graph Neural Networks are increasingly employed in real-world applications, it becomes important to ensure that these representations are fair; graph embeddings can in fact encode potentially harmful social biases, such as the information that women are more likely to be nurses, and men more likely to be bankers. This work explores and enforces state of the art methods to mitigate this bias and produce fairer representations of real world graph data while maintaining a good classification accuracy.