  We study the task of node classification for graph neural networks (GNNs) and
establish a connection between group fairness, as measured by statistical
parity and equal opportunity, and local assortativity, i.e., the tendency of
linked nodes to have similar attributes. Such assortativity is often induced by
homophily, the tendency for nodes of similar properties to connect. Homophily
can be common in social networks where systemic factors have forced individuals
into communities which share a sensitive attribute. Through synthetic graphs,
we study the interplay between locally occurring homophily and fair
predictions, finding that not all node neighborhoods are equal in this respect
-- neighborhoods dominated by one category of a sensitive attribute often
struggle to obtain fair treatment, especially in the case of diverging local
class and sensitive attribute homophily. After determining that a relationship
between local homophily and fairness exists, we investigate if the issue of
unfairness can be associated to the design of the applied GNN model. We show
that by adopting heterophilous GNN designs capable of handling disassortative
group labels, group fairness in locally heterophilous neighborhoods can be
improved by up to 25% over homophilous designs in real and synthetic datasets.
