  Spectral clustering is popular among practitioners and theoreticians alike.
While performance guarantees for spectral clustering are well understood,
recent studies have focused on enforcing ``fairness'' in clusters, requiring
them to be ``balanced'' with respect to a categorical sensitive node attribute
(e.g. the race distribution in clusters must match the race distribution in the
population). In this paper, we consider a setting where sensitive attributes
indirectly manifest in an auxiliary \textit{representation graph} rather than
being directly observed. This graph specifies node pairs that can represent
each other with respect to sensitive attributes and is observed in addition to
the usual \textit{similarity graph}. Our goal is to find clusters in the
similarity graph while respecting a new individual-level fairness constraint
encoded by the representation graph. We develop variants of unnormalized and
normalized spectral clustering for this task and analyze their performance
under a \emph{fair} planted partition model induced by the representation
graph. This model uses both the cluster membership of the nodes and the
structure of the representation graph to generate random similarity graphs. To
the best of our knowledge, these are the first consistency results for
constrained spectral clustering under an individual-level fairness constraint.
Numerical results corroborate our theoretical findings.
