  Learning and reasoning over graphs is increasingly done by means of
probabilistic models, e.g. exponential random graph models, graph embedding
models, and graph neural networks. When graphs are modeling relations between
people, however, they will inevitably reflect biases, prejudices, and other
forms of inequity and inequality. An important challenge is thus to design
accurate graph modeling approaches while guaranteeing fairness according to the
specific notion of fairness that the problem requires. Yet, past work on the
topic remains scarce, is limited to debiasing specific graph modeling methods,
and often aims to ensure fairness in an indirect manner.
  We propose a generic approach applicable to most probabilistic graph modeling
approaches. Specifically, we first define the class of fair graph models
corresponding to a chosen set of fairness criteria. Given this, we propose a
fairness regularizer defined as the KL-divergence between the graph model and
its I-projection onto the set of fair models. We demonstrate that using this
fairness regularizer in combination with existing graph modeling approaches
efficiently trades-off fairness with accuracy, whereas the state-of-the-art
models can only make this trade-off for the fairness criterion that they were
specifically designed for.
