  Graph representation learning is central for the application of machine
learning (ML) models to complex graphs, such as social networks. Ensuring
`fair' representations is essential, due to the societal implications and the
use of sensitive personal data. In this paper, we demonstrate how the
parametrization of the \emph{CrossWalk} algorithm influences the ability to
infer a sensitive attributes from node embeddings. By fine-tuning
hyperparameters, we show that it is possible to either significantly enhance or
obscure the detectability of these attributes. This functionality offers a
valuable tool for improving the fairness of ML systems utilizing graph
embeddings, making them adaptable to different fairness paradigms.
